Nikolaos G. Bourbakis

Wright State University, Dayton, Ohio, United States

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Publications (248)127.62 Total impact

  • [Show abstract] [Hide abstract]
    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.
    5th International Conference on Information, Intelligence, Systems and Applications, Greece; 07/2014
  • 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.
    International Journal of Artificial Intelligence Tools 12/2013; 22(06). DOI:10.1142/S0218213013500309 · 0.32 Impact Factor
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    ABSTRACT: Offering an alternative mode of interaction with the surrounding 3-D space to the visually impaired for collision free navigation is a goal of great significance that includes several key challenges. In this paper, we study the alternative 3-D space sensation that is interpreted by our computer vision prototype system and transferred to the user via a vibration array. There are two main tasks for conducting such a study. The first task is to detect obstacles in close proximity, and motion patterns in image sequences, both important issues for a safe navigation in a 3-D dynamic space. To achieve this task, the images from the left and right cameras are acquired to produce new stereo images, followed by video stabilization as a preprocessing stage, a nonlinear spatio-temporal diffusion and kernel based density estimation method to assess the motion activity, and finally watershed-based detection of moving regions (or obstacles) of interest. The second task is to efficiently represent the information of the captured static and dynamic visual scenes as 3-D detectable patterns of vibrations applied on the human body to create a 3-D sensation of the space during navigation. To accomplish this task, considering the current limitations imposed by the technology, we create a high-to-low (H-L) image resolution representation scheme to facilitate the mapping onto a low-resolution 2-D array of vibrators. The H-L scheme uses pyramidal modeling to obtain low-resolution images of interest-preserving motion and obstacles-that are mapped onto a vibration array. These patterns are utilized to train and test the performance of the users in free space navigation. Thus, in this paper we study the synergy of these two important schemes to offer an alternative sensation of the 3-D space to the visually impaired via an array of vibrators. Particularly, the motion component is employed as an element for the identification of visual information of interest to be retained during the H-L tran- formation. The role of the array vibrators is to create a small-scale front representation of the space via various levels of vibrations. Thus, 3-D vibrations applied on the user's body (chest, abdomen) offer a 3-D sensation of the surrounding space and the motion in it. In addition, we present experimental results that indicate the efficiency of this navigation scheme in creating low-resolution 3-D views of the free navigation space and detecting obstacles and moving areas.
    IEEE Sensors Journal 07/2013; 13(7):2535-2547. DOI:10.1109/JSEN.2013.2253092 · 1.85 Impact Factor
  • Nikolaos Bourbakis
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    ABSTRACT: Detecting faces and facial expressions has become a common task in human-computer interaction systems. A face-facial detection system must be able to detect faces under various conditions and extract their facial expressions. Many approaches for face detection have been proposed in the literature mainly dealing with the detection or recognition of faces in still conditions rather than the person's facial expressions and the reflecting emotional behavior. In this paper, the author describes a synergistic methodology for detecting frontal high-resolution color faces and for recognizing their facial expressions accurately in realistic conditions both indoor and outdoor, and with a variety of conditions shadows, high-lights, non-white lights. The methodology associates these facial expressions to emotional behavior. It extracts important facial features, such as eyes, eyebrows, nose, mouth lips and defines them as the primitive elements of an alphabet of a simple formal language in order to synthesize these facial features and generate emotional expressions. The main goal of this effort is to monitor emotional behavior and learn from it. Illustrative examples are also provided for proving the concept of the methodology.
    04/2013; 1(2):1-28. DOI:10.4018/ijmstr.2013040101
  • Robert Keefer, Yan Liu, Nikolaos Bourbakis
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    ABSTRACT: Research into eyes-free mobile reading devices has grown in recent years. This research has focused mainly on the image processing required by such a device, with a lower emphasis on the user interaction. In this paper, a model of a voice user interface (VUI) for a mobile reading device is presented. Three field studies with blind participants were conducted to develop and refine the model. A formal grammar is used to describe the VUI, and a stochastic Petri net was developed to model the complete user-device interaction. Evaluation and analysis of the user testing of a prototype led to empirically derived probabilities of grammar token usage for the commands that comprise the VUI.
    01/2013; 43(1):76-91. DOI:10.1109/TSMCA.2012.2210413
  • A. Tsitsoulis, N. Bourbakis
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    ABSTRACT: Extraction of the human body in single, unconstrained, monocular images is a very difficult task. Localization and extraction of the body region, however, provides important and useful knowledge that can facilitate many other tasks, such as gesture recognition, pose estimation and action recognition. In this paper we present a simple appearance-based methodology that combines face detection, skin detection, image segmentation and anthropometric constraints to efficiently estimate the position and regions of hands in images. It requires no training neither explicit estimation of the human pose. Experimental results in a difficult dataset illustrate the performance of the approach.
    Information, Intelligence, Systems and Applications (IISA), 2013 Fourth International Conference on; 01/2013
  • Athanasios Tsitsoulis, Nikolaos Bourbakis
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    ABSTRACT: Detecting faces has become a common task in human-computer interaction systems. A robust face detection system should be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, facial pose, orientation and facial expressions. In this paper we describe a method comprising color constancy based skin detection and Local-Global (LG) graph matching in color images to address these challenges. Illustrative examples are also provided as a proof of concept.
    Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence - Volume 01; 11/2012
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    ABSTRACT: Over the past decade Wireless Capsule Endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Using WCE physicians can examine the digestive tract in a minimum invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers and Crohn's disease. In order for WCE to be more effective for gastroenterologists, engineers have developed software methods to automatically detect these diseases at high successful rate. Using proposed a synergistic methodology for automatic discovering polyps (protrusions) and ulcers in WCE video frames, a data mining approach is used that offers useful information about ulcers, polyps and normal tissues and their visual similarities. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.
    International Journal of Artificial Intelligence Tools 11/2012; 21(05). DOI:10.1142/S0218213012400210 · 0.32 Impact Factor
  • Athanasios Tsitsoulis, Nikolaos Bourbakis
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    ABSTRACT: Image segmentation is one of the first important parts of image analysis and understanding. Evaluation of image segmentation, however, is a very difficult task, mainly because it requires human intervention and interpretation. In this work, we propose a blind reference evaluation scheme based on regional local–global (RLG) graphs, which aims at measuring the amount and distribution of detail in images produced by segmentation algorithms. The main idea derives from the field of image understanding, where image segmentation is often used as a tool for scene interpretation and object recognition. Evaluation here derives from summarization of the structural information content and not from the assessment of performance after comparisons with a golden standard. Results show measurements for segmented images acquired from three segmentation algorithms, applied on different types of images (human faces/bodies, natural environments and structures (buildings)).
    Measurement Science and Technology 10/2012; 23(11):114007. DOI:10.1088/0957-0233/23/11/114007 · 1.35 Impact Factor
  • Grigorios Chrysos, Apostolos Dollas, Nikolaos Bourbakis
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    ABSTRACT: Image segmentation is one of the first important and difficult steps of image analysis and computer vision and it is considered as one of the oldest problems in machine vision. Lately, several segmentation algorithms have been developed with features related to thresholding, edge location and region growing to offer an opportunity for the development of faster image/video analysis and recognition systems. In addition, fuzzy-based segmentation algorithms have essentially contributed to synthesis of regions for better representation of objects. These algorithms have minor differences in their performance and they all perform well. Thus, the selection of one algorithm vs. another will be based on subjective criteria, or, driven by the application itself. Here, a low-cost embedded reconfigurable architecture for the Fuzzy-like reasoning segmentation (FRS) method is presented. The FRS method has three stages (smoothing, edge detection and the actual segmentation). The initial smoothing operation is intended to remove noise. The smoother and edge detector algorithms are also included in this processing step. The segmentation algorithm uses edge information and the smoothed image to find segments present within the image. In this work the FRS segmentation algorithm was selected due to its proven good performance on a variety of applications (face detection, motion detection, Automatic Target Recognition (ATR)) and has been developed in a low-cost, reconfigurable computing platform, aiming at low cost applications. In particular, this paper presents the implementation of the smoothing, edge detection and color segmentation algorithms using Stretch S5000 processors and compares them with a software implementation using the Matlab. The new architecture is presented in detail in this work, together with results from standard benchmarks and comparisons to alternative technologies. This is the first such implementation that we know of, having at the same time high throughput, excellent performance (at least in standard benchmarks) and low cost.
    Microprocessors and Microsystems 05/2012; 36(3):215–231. DOI:10.1016/j.micpro.2011.12.004 · 0.60 Impact Factor
  • Athanasios Tsitsoulis, Ryan Patrick, Nikolaos Bourbakis
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    ABSTRACT: A surveillance system for assisting the elderly in remaining independent in their familiar environment is one of the subjects interest in recent healthcare studies. When mature, it is expected that this system will have the ability to track objects that a resident may lose periodically, detect falls within the home, alert family members or healthcare professionals to abnormal behaviors. This paper addresses the early stages and issues of the development of such a system, the physical characteristics of the system that is being designed, early results, and guidance on the future work that will have to be completed in the future.
    Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on; 01/2012
  • Alexandros Karargyris, Nikolaos Bourbakis
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    ABSTRACT: Over the last decade, wireless capsule endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Physicians using WCE can examine the digestive tract in a minimally invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers, and Crohn's disease. To improve effectiveness of WCE, researchers have developed software methods to automatically detect these diseases at a high rate of success. This paper proposes a novel synergistic methodology for automatically discovering polyps (protrusions) and perforated ulcers in WCE video frames. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.
    IEEE Transactions on Biomedical Engineering 11/2011; 58(10-58):2777 - 2786. DOI:10.1109/TBME.2011.2155064 · 2.23 Impact Factor
  • Raghudeep Kannavara, Sukarno Mertoguno, Nikolaos Bourbakis
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    ABSTRACT: This paper presents the design of the SCAN secure processor and its extended instruction set to enable secure biometric authentication. The SCAN secure processor is a modified SparcV8 processor architecture with a new instruction set to handle voice, iris, and fingerprint-based biometric authentication. The algorithms for processing biometric data are based on the local global graph methodology. The biometric modules are synthesized in reconfigurable logic and the results of the field-programmable gate array (FPGA) synthesis are presented. We propose to implement the above-mentioned modules in an off-chip FPGA co-processor. Further, the SCAN-secure processor will offer a SCAN-based encryption and decryption of 32 bit instructions and data.
    Journal of Electronic Imaging 04/2011; 20(2). DOI:10.1117/1.3582930 · 0.85 Impact Factor
  • Alexandros Pantelopoulos, Nikolaos G. Bourbakis
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    ABSTRACT: Wearable health monitoring systems (WHMS) enable ubiquitous and unobtrusive monitoring of a variety of vital signs that can be measured non-invasively. These systems have the potential to revolutionize healthcare delivery by achieving early detection of critical health changes and thus possibly even disease or hazardous event prevention. Amongst the patient populations that can greatly benefit from WHMS are Congestive Heart Failure (CHF) patients. For CHF management the detection of heart arrhythmias is of crucial importance. However, since WHMS have limited computing and storage resources, diagnostic algorithms need to be computationally inexpensive. Towards this goal, we investigate in this paper the efficiency of the Matching algorithm in deriving compact time-frequency representations of ECG data, which can then be utilized from an Artificial Neural Network (ANN) to achieve beat classification. In order to select the most appropriate decomposition structure, we examine the effect of the type of dictionary utilized (stationary wavelets, cosine packets, wavelet packets) in deriving optimal features for classfication. Our results show that by applying a greedy algorithm to determine the dictionary atoms that show the greatest correlation with the ECG morphologies, an accurate, efficient and real-time beat classification scheme can be derived. Such an algorithm can then be inexpensively run on a resource-constrained portable device such as a cell phone or even directly on a smaller microcontroller-based board. The performance of our approach is evaluated using the MIT-BIH Arrhythmia database. Provided results illustrate the accuracy of the proposed method (94.9%), which together with its simplicity (a single linear transform is required for feature extraction) justify its use for real-time classification of abnormal heartbeats on a portable heart monitoring system.
    IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011; 01/2011
  • N. Bourbakis, A. Tsitsoulis
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    ABSTRACT: Image segmentation is one of the first important parts of the image analysis and understanding. Theo Pavlidis initially introduced it in late 60s, and since then researchers have studied it and produced a great variety of algorithms. These algorithms provide segmentation results that could be characterized from “objective”, like Pavlidis segmentation to “subjective” ones dependent on the interpretation of the human user. In this paper we attempt to initiate a first stage evaluation of color image segmentation methodologies by proposing a simple segmentation metric. In particular, we have used three segmentation algorithms on different types of images (human faces/bodies, natural environments, and structures (buildings). Then we apply the metric (formula) on these segmented results in order to quickly observe and evaluate the performance (behavior) of each segmentation methodology.
    01/2011; DOI:10.1109/NAECON.2011.6183114
  • Alexandros Karargyris, Nikolaos G. Bourbakis
  • Nikolaos G. Bourbakis, Sokratis Makrogiannis, D. Kapogiannis
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    ABSTRACT: It is known that the early detection of chronic diseases significantly increases the life span of the elderly and improves the quality of life in general. Since the brain is the most valuable part of the human body, it is very important for the physicians to know the stages of aging and changes that take place during these stages and possible implications associated with them in order to more effectively treat their patients. In addition, fMRI provides information (size, density, location, etc) from brain images for the active regions during thinking and/or performing certain tasks. Thus, in this paper a monitoring brain-aging model based on a synergy of methodologies, like image segmentation, registration, local global (L-G) graphs and stochastic Petri net (SPN) graphs is presented. In particular, the synergistic brain-aging model uses fMRI images to detect, extract and associate the way that the brain regions interact regarding thinking and/or executing certain tasks. These brain-region images are extracted and geometrically are represented and associated with the L-G graphs. Then the use of SPN graphs models the regions' functionality. Thus, comparing the L-G and SPN graphs extracted from fMRI images taken in different periods from the same subject, the model has the capability to detect changes and associate them in order the medical expert to monitor the health status and provide a diagnosis or prognosis regarding a human subject. Sets of L-G and SPN graph models generated in time for each particular subject are available in an L-G/SPN graph Database. Here the synergistic model and its proof of concept are presented.
    IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011; 01/2011
  • Source
    Stejskal V, Bourbakis N, Anna Esposito
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    ABSTRACT: This work describes two new pause detection algorithms and compare their performance with four standard Voice Activity Detection (VAD) methods represented by the adaptive Long Term Spectral Divergence (LTSD) algorithm, the Likelihood Ratio Test (LRT) algorithm, the Neural Network thresholding and G.729.
    Journal of Advanced Computational Intelligence and Intelligent Informatics 01/2011; 2(1):145-160.
  • Alexandros Karargyris, Nikolaos Bourbakis
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    ABSTRACT: Wireless capsule endoscopy is a revolutionary technology that allows physicians to examine the digestive tract of a human body in the minimum invasive way. Physicians can detect diseases such as blood-based abnormalities, polyps, ulcers, and Crohn's disease. Although this technology is really a marvel of our modern times, currently it suffers from two serious drawbacks: 1) frame rate is low (3 frames/s) and 2) no 3-D representation of the objects is captured from the camera of the capsule. In this paper we offer solutions (methodologies) that deal with each of the above issues improving the current technology without forcing hardware upgrades. These methodologies work synergistically to create smooth and visually friendly interpolated images from consecutive frames, while preserving the structure of the observed objects. They also extract and represent the texture of the surface of the digestive tract in 3-D. Thus the purpose of our methodology is not to reduce the time that the gastroenterologists need to spend to examine the video. On the contrary, the purpose is to enhance the video and therefore improve the viewing of the digestive tract leading to a more qualitative and efficient examination. The proposed work introduces 3-D capsule endoscopy textured results that have been welcomed by Digestive Specialists, Inc., Dayton, OH. Finally, illustrative results are given at the end of the paper.
    12/2010; 30(4):957-71. DOI:10.1109/TMI.2010.2098882
  • Alexandros Pantelopoulos, Nikolaos Bourbakis
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    ABSTRACT: In this paper we present our efforts towards establishing a wearable platform that utilizes off-the-shelf Bluetooth-enabled sensors in order to preprocess and analyze streaming physiological recordings. A smart-phone running multi-threaded J2ME software is utilized for handling multiple simultaneous Bluetooth connections and a network socket connection with a remote workstation. Received measurements of signals such as the ECG and PPG are decomposed through appropriately selected Wavelet Transforms with the purpose of identifying unusable segments that have been severely corrupted by noise and de-noising the remaining usable data portions. We study the use of the undecimated wavelet packet transform for ECG noise removal. Provided results illustrate the advantages of the proposed decomposition for wavelet denoising compared to conventional approaches, at the cost however of performing more computations. The described wearable platform along with the documented data preprocessing steps is employed as the front end of a closed-loop intelligent and interactive system termed Prognosis. This system is capable of facilitating ubiquitous and unsupervised round-the-clock health monitoring of people at risk, as it is able to a) address the issue of unsupervised data collection and b) to interact with the patient via an automated speech-dialogue system.

Publication Stats

2k Citations
127.62 Total Impact Points

Institutions

  • 2001–2013
    • Wright State University
      • Department of Computer Science and Engineering
      Dayton, Ohio, United States
  • 2009
    • King's College London
      Londinium, England, United Kingdom
  • 2007–2009
    • Jacksonville State University
      Alabama, United States
  • 2008
    • WSU West
      Seattle, Washington, United States
  • 2004–2008
    • University of Nevada, Reno
      • Department of Computer Science and Engineering
      Reno, NV, United States
  • 2005
    • Wayne State University
      Detroit, Michigan, United States
  • 2003–2005
    • Technical University of Crete
      • Department of Electronic and Computer Engineering
      Chaniá, Kriti, Greece
  • 1993–2001
    • Binghamton University
      • • Department of Electrical and Computer Engineering
      • • Center for Intelligent Systems
      Binghamton, New York, United States
  • 1999
    • Tulane University
      New Orleans, Louisiana, United States
    • State University of New York
      New York City, New York, United States
  • 1997
    • Purdue University
      • School of Nuclear Engineering
      West Lafayette, IN, United States
  • 1996–1997
    • University of Crete
      Retimo, Crete, Greece
  • 1992–1995
    • 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