Nikolaos G. Bourbakis

Wright State University, Dayton, Ohio, United States

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Publications (248)205.85 Total impact

  • Kostas Michalopoulos · Nikolaos Bourbakis
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    ABSTRACT: Combining information from Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) has been a topic of increased interest recently. The main advantage of the EEG is its high temporal resolution, in the scale of milliseconds, while the main advantage of fMRI is the detection of functional activity with good spatial resolution. The advantages of each modality seem to complement each other, providing better insight in the neuronal activity of the brain. The main goal of combining information from both modalities is to increase the spatial and the temporal localization of the underlying neuronal activity captured by each modality. This paper presents a novel technique based on the combination of these two modalities (EEG, fMRI) that allow a better representation and understanding of brain activities in time. EEG is modeled as a sequence of topographies, based on the notion of microstates. Hidden Markov Models (HMMs) were used to model the temporal evolution of the topography of the average Event Related Potential (ERP). For each model the Fisher score of the sequence is calculated by taking the gradient of the trained model parameters. The Fisher score describes how this sequence deviates from the learned HMM. Canonical Partial Least Squares (CPLS) were used to decompose the two datasets and fuse the EEG and fMRI features. In order to test the effectiveness of this method, the results of this methodology were compared with the results of CPLS using the average ERP signal of a single channel. The presented methodology was able to derive components that co-vary between EEG and fMRI and present significant differences between the two tasks.
    No preview · Article · Oct 2015 · International Journal of Neural Systems
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    ABSTRACT: Presents a summary of the articles included in this issue of the publication that focus on haptic assistive technology for people that are visually impaired.
    Full-text · Article · Jul 2015 · IEEE Transactions on Haptics

  • No preview · Conference Paper · Jul 2015
  • Athanasios Tsitsoulis · Nikolaos G. Bourbakis
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    ABSTRACT: Segmentation of human bodies in images is a challenging task that can facilitate numerous applications, like scene understanding and activity recognition. In order to cope with the highly dimensional pose space, scene complexity, and various human appearances, the majority of existing works require computationally complex training and template matching processes. We propose a bottom-up methodology for automatic extraction of human bodies from single images, in the case of almost upright poses in cluttered environments. The position, dimensions, and color of the face are used for the localization of the human body, construction of the models for the upper and lower body according to anthropometric constraints, and estimation of the skin color. Different levels of segmentation granularity are combined to extract the pose with highest potential. The segments that belong to the human body arise through the joint estimation of the foreground and background during the body part search phases, which alleviates the need for exact shape matching. The performance of our algorithm is measured using 40 images (43 persons) from the INRIA person dataset and 163 images from the “lab1” dataset, where the measured accuracies are 89.53% and 97.68%, respectively. Qualitative and quantitative experimental results demonstrate that our methodology outperforms state-of-the-art interactive and hybrid top-down/bottom-up approaches.
    No preview · Article · Jun 2015 · IEEE Transactions on Human-Machine Systems
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    ABSTRACT: Price-directed demand in smart grids operating within deregulated electricity markets calls for real-time forecasting of the price of electricity for the purpose of scheduling demand at the nodal level (e.g., appliances, machines, and devices) in a way that minimizes energy cost to the consumer. In this paper, a novel hybrid methodology for electricity price forecasting is introduced and applied on a set of real-world historical data taken from the New England area. The proposed approach is implemented in two steps. In the first step, a set of relevance vector machines (RVMs) is adopted, where each RVM is used for individual ahead-of-time price prediction. In the second step, individual predictions are aggregated to formulate a linear regression ensemble, whose coefficients are obtained as the solution of a single objective optimization problem. Thus, an optimal solution to the problem is found by employing the micro-genetic algorithm and the optimized ensemble is employed for computing the final price forecast. The performance of the proposed methodology is compared with performance of autoregressive-moving-average and naïve forecasting methods, as well as to that taken from each individual RVM. Results clearly demonstrate the superiority of the hybrid methodology over the other tested methods with regard to mean absolute error for electricity signal pricing forecasting.
    No preview · Article · May 2015 · IEEE Transactions on Smart Grid
  • Michail Tsakalakis · Nikolaos G. Bourbakis
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    ABSTRACT: In the present paper we have focused our efforts in de-noising/despeckling methods for ultrasound images based on a novel multi - transducer architecture. Frequency compounding schemes have been implemented and tested in two different images and the results are compared with the state of the art software based methods for ultrasound image despeckling. Signal - to - noise ratio, SNR, peak - signal - to - noise ratio, PSNR, mean square error, MSE, and the mutual information measures were used to assess the performance of each method. Furthermore, a general scheme for ultrasound image processing based on the notion of sup resolution technique that combines multiple low resolution, LR, images of the same scene to produce one high resolution, HR, image, used in camera technology, is proposed. The nature of the system's architecture has made possible the use of simple but effective compounding techniques for speckle reduction while at the same time sup resolution approach may produce ultrasound images of better quality and higher resolution. Last but not least, the article provides an insight to the major problems and drawbacks of ultrasound imaging systems and how the proposed architecture is capable of effectively dealing with these problems.
    No preview · Article · Feb 2015
  • Ryan Patrick · Nikolaos Bourbakis

    No preview · Article · Jan 2015
  • Robert Keefer · Nikolaos Bourbakis
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    ABSTRACT: This paper offers a review of the state-of-the-art document image processing methods and their classification by identifying new trends for automatic document processing and understanding. Document image processing (DIP) is an important problem related with most of the challenges coming from the image processing field and with applications to digital document summarization, readers for the visually impaired etc. Difficulties in the processing of documents can arise from lighting conditions, page curl, page rotation in 3D, and page layout segmentation. Document image processing is usually performed in the context of higher-level applications that require an undistorted document image such as optical character recognition and document restoration/preservation. Typically, assumptions are made to constrain the processing problem in the context of a particular application. In this survey, we categorize document image processing methods on the basis of the technique, provide detailed descriptions of representative methods in each category, and examine their pros and cons. It important to notice here that the DIP field is broad, thus we try to provide a top-down/horizontal survey rather a bottom up. At the same time, we target the area of document readers for the blind, and use this application to guide us in a top-down survey of DIP. Moreover, we present a comparative survey based on important aspects of a marketable system that is dependent on document image processing techniques.
    No preview · Article · Jan 2015 · International Journal of Image and Graphics
  • Anna Trikalinou · Nikolaos Bourbakis

    No preview · Article · Jan 2015

  • No preview · Article · Oct 2014
  • Article: Demos:

    No preview · Article · Oct 2014
  • Nikolaos Bourbakis · Alexandros Karargyris
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    ABSTRACT: This chapter offers a survey of wireless capsule endoscopy (WCE) and endoscopic imaging by evaluating some representative methodologies as well as that WCE technology is also used in colonoscopy. The detection of various organs inside the digestive tract is an important tool for the physician. The chapter presents challenges related to the WCE approaches. In diagnostic endoscopy, based on a distributed perception of local changes, the medical expert interprets the physical surface properties of the tissue, such as the roughness or the smoothness, the regularity, and the shape, to detect abnormalities. According to Fireman and Kopelman (2010): “Researchers have proposed the idea that endoscopists will be able to ‘control and steer’ the Capsule Endoscopy (CE), as they are able to do in standard endoscopy.” The chapter also proposes the design of a WCE robotic capsule for diagnostic/therapeutic endoscopy.
    No preview · Chapter · Aug 2014
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    ABSTRACT: Digital security has always been an important concern in computer systems with the advent of the Internet, sharing of information and services between distant points never being easier before. Threats to security in computer systems can be classified into four broad categories: interception, interruption, modification, and fabrication. This chapter presents a prototype wearable platform based on commercially available Bluetooth-enabled wearable sensors and a BlackBerry Smartphone. It describes how recorded physiological data can be analyzed continuously through appropriate methods based on DWT in order to detect signal portions of the ECG and PPG corrupted by noise and to denoise the recorded signals. The chapter also describes the secure way of protecting health information during exchange with a remote center.
    No preview · Chapter · Aug 2014
  • Kostas Michalopoulos · Nikolaos Bourbakis
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    ABSTRACT: In this work we present a methodology for modeling the trajectory of EEG topography over time, using Dynamic Bayesian Networks (DBNs). Based on the microstate model we are using DBNs to model the evolution of the EEG topography. Analysis of the microstate model is being usually limited in the wide band signal or an isolated band. We are using Coupled Hidden Markov Models (CHMM) and a two level influence model in order to model the temporal evolution and the coupling of the topography states in three bands, delta, theta and alpha. We are applying this methodology for the classification of target and non-target single trial from a visual detection task. The results indicate that taking under consideration the interaction among the different bands improves the classification of single trials.
    No preview · Article · Aug 2014
  • Miltiadis Alamaniotis · Lefteri H. Tsoukalas · Nikolaos Bourbakis
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    ABSTRACT: In this paper a new approach for electricity consumption scheduling for smart grids/cities in price-directed electricity markets is presented. The new approach, named virtual cost, integrates electricity price forecasting and load forecasting to obtain an optimal consumption pattern. The goal of virtual cost approach is to provide the springboard for automated consumption scheduling with minimum human intervention, while reducing the expenses for electricity purchasing. Towards that end, the proposed approach takes into consideration three factors: the anticipated electricity price for a day-ahead horizon, the respective anticipated load and an upper limit for expenses that is manually input by the consumer. Integration of these factors allows the intelligent meter through which the consumer is connected to smart grid to make decisions over load consumption scheduling aiming at reducing cost. The proposed approach is tested on real world data taken from the New England area. Results exhibit a reduction in consumption expenses, indicating the potentiality of the virtual cost approach as an automated scheduling tool for electricity consumption in price directed electricity markets.
    No preview · Conference Paper · Jul 2014
  • Anna Trikalinou · Nikolaos Bourbakis
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    ABSTRACT: Security has always been an important concern in Computer Systems. In this paper we focus on zero-day, memory-based attacks, one of the top three most dangerous attacks according to the MITRE ranking, and propose AMYNA, a novel security generator framework/model., which can automatically create personalized optimum security solutions. Motivated by the most prevailing security methods, which target a limited set of attacks, but do so efficiently and effectively, we present the idea and architecture of AMYNA, which can automatically combine security methods represented in a high-level model in order to produce a security solution with elevated security coverage.
    No preview · Conference Paper · Jul 2014
  • Stavros Mallios · Nikolaos Bourbakis
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    ABSTRACT: The general field of health monitoring and health information exchange has garnered a keen research and market interest for the past few decades. This is indicated by the development of a plethora of wearable health monitoring systems, as well as research efforts and projects concerning the security issues in health information exchange. In this paper, a virtual doctor prototype for quick diagnosis and secure health information exchange is presented. The measured physiological data that are acquired by a wearable health monitoring system are modeled as words of a fuzzy formal language and are used by a trained and personalized neural network to diagnose the patient's health status. Moreover, we present a secure architectural scheme, which secures the transmitted data and uses the medical doctors' biometrics to authorize them and give them access to these data.
    No preview · Conference Paper · Jul 2014
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    ABSTRACT: Applying engineering precepts to biological systems has spawn the field of systems biology to investigate a network of interacting components, including the coordination of internal systems of living organisms such as endocrine, nervous, and respiratory with gene and gene product expression, and behavior and environmental factors, and understand how these components together contribute to the disease initiation and progression, biological development, and health. Proceeding from systems biology, systems medicine incorporates complex and dynamic biochemical, physiological, and environmental interactions between all components of disease and health that sustain living organisms. The current special issue includes a selected number of papers presented at the 12th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2012), Nov. 11-13, 2012 under a special session with the same theme, in addition to papers submitted following an open call for papers. The Special Issue presents experiences as well as technological and scientific developments stemming from some flagship projects funded by the EU under the FP7 framework programme aiming to bring together researchers working in the fields of infrastructures and technologies for integrative biomedical research, ICT for predictive and translational medicine and the VPH community at large. A total of 15 papers are included under the following scientific subdomains: 1) mHealth,Wearable Systems and Telemonitoring Services (five papers), 2) Medical Imaging (four papers), and 3) Computational Biology (six papers).
    Full-text · Article · May 2014 · IEEE Journal of Biomedical and Health Informatics
  • Michael T. Mills · Nikolaos G. Bourbakis
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    ABSTRACT: This survey and analysis presents the functional components, performance, and maturity of graph-based methods for natural language processing and natural language understanding and their potential for mature products. Resulting capabilities from the methods surveyed include summarization, text entailment, redundancy reduction, similarity measure, word sense induction and disambiguation, semantic relatedness, labeling (e.g., word sense), and novelty detection. Estimated scores for accuracy, coverage, scalability, and performance are derived from each method. This survey and analysis, with tables and bar graphs, offers a unique abstraction of functional components and levels of maturity from this collection of graph-based methodologies.
    No preview · Article · Jan 2014 · IEEE Transactions on Systems, Man, and Cybernetics: Systems
  • Athanasios Tsitsoulis · Nikolaos Bourbakis
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    ABSTRACT: The development of vision-based human activity recognition and analysis systems has been a matter of great interest to both the research community and practitioners during the last 20 years. Traditional methods that require a human operator watching raw video streams are nowadays deemed as at least ineffective and expensive. New, smart solutions in automatic surveillance and monitoring have emerged, propelled by significant technological advances in the fields of image processing, artificial intelligence, electronics and optics, embedded computing and networking, molding the future of several applications that can benefit from them, like security and healthcare. The main motivation behind it is to exploit the highly informative visual data captured by cameras and perform high-level inference in an automatic, ubiquitous and unobtrusive manner, so as to aid human operators, or even replace them. This survey attempts to comprehensively review the current research and development on vision-based human activity recognition. Synopses from various methodologies are presented in an effort to garner the advantages and shortcomings of the most recent state-of-the-art technologies. Also a first-level self-evaluation of methodologies is also proposed, which incorporates a set of significant features that best describe the most important aspects of each methodology in terms of operation, performance and others and weighted by their importance. The purpose of this study is to serve as a reference for further research and evaluation to raise thoughts and discussions for future improvements of each methodology towards maturity and usefulness.
    No preview · Article · Dec 2013 · International Journal of Artificial Intelligence Tools

Publication Stats

3k Citations
205.85 Total Impact Points

Institutions

  • 2001-2015
    • Wright State University
      • Department of Computer Science and Engineering
      Dayton, Ohio, United States
    • Technical University of Crete
      • Department of Electronic and Computer Engineering
      La Canée, Crete, Greece
  • 2009
    • King's College London
      Londinium, England, United Kingdom
  • 2001-2008
    • WSU West
      Seattle, Washington, United States
  • 2007
    • Jacksonville State University
      Джексонвилл, Alabama, United States
  • 2002
    • Tulane University
      New Orleans, Louisiana, United States
  • 1993-2001
    • Binghamton University
      • • Department of Electrical and Computer Engineering
      • • Center for Intelligent Systems
      Binghamton, New York, United States
  • 1997-1999
    • University of Crete
      Retimo, Crete, Greece
  • 1997-1998
    • Purdue University
      • • Department of Computer Science
      • • School of Nuclear Engineering
      ウェストラファイエット, Indiana, United States
  • 1995-1998
    • University of Patras
      Rhion, West Greece, Greece
  • 1990-1991
    • IBM
      Armonk, New York, United States
    • Australian National University
      Canberra, Australian Capital Territory, Australia
  • 1985-1989
    • George Mason University
      • Department of Electrical and Computer Engineering
      Fairfax, VA, United States