Top SIP Research Articles of 2019

Top SIP Research Articles of 2019
International Journal of VLSI design &
Communication Systems (VLSICS)
ISSN : 0976 - 1357 (Online); 0976 - 1527(print)
http://airccse.org/journal/vlsi/vlsics.html

COLOR CONVERTING OF ENDOSCOPIC IMAGES USING
DECOMPOSITION THEORY AND PRINCIPAL COMPONENT
ANALYSIS
Keivan Ansari1,2
, Alexandre Krebs1
, Yannick Benezeth1
and Franck Marzani1
1
ImViA-Imaging and Artificial Vision,Université de Bourgogne, Dijon, France
2
Dept. Color Imaging and Color Image Processing, Institute for Color Science and Technology, Tehran,
Iran
ABSTRACT
Endoscopic color imaging technology has been a great improvement to assist clinicians in making better
decisions since the initial introduction. In this study, a novel combined method, including quadratic
objective functions for the dichromatic model by Krebs et al. and Wyszecki`s spectral decomposition
theory and the well-known principal component analysis technique is employed. New algorithm method
working for color space converting of a conventional endoscopic color image, as a target image, with a
Narrow Band Image (NBI), as a source image. The images of the target and the source are captured under
known illuminant/sensor/filters combinations, and matrix Q of the decomposition theory is computed for
such combinations. The intrinsic images which are extracted from the Krebs technique are multiplied by
the matrix Q to obtain their corresponding fundamental stimuli. Subsequently, the principal component
analysis technique was applied to the obtained fundamental stimuli in order to prepare the eigenvectors of
the target and the source. Finally, the first three eigenvectors of each matrix were then considered as the
converting mapping matrix. The results precisely seem that the color gamut of the converted target image
gets closer to the NBI image color gamut.
KEYWORDS
Color Converting, Endoscopic Imaging, Dichromatic Model, Principal Component Analysis,
Decomposition Theory.
Full Text : https://aircconline.com/csit/papers/vol9/csit91812.pdf
9th
International Conference on Computer Science, Engineering and Applications
(ICCSEA 2019) - http://airccse.org/csit/V9N18.html

REFERENCES
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Engineering Research and General Science Vol. 2, Issue 6, Oct. -Nov.

[15] A. Krebs, Y. Benezeth, F. Marzani, (2017)“Quadratic objective functions for dichromatic model
parameters estimation,” in: IEEE International Conference on Digital Image Computing: Techniques and
Applications (DICTA).
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the Optical Society of America, Vol. 31,Issue 4, pp. A38-A46.
[19] Y. Mohamed, Y. Abdallah and T.Alqahtani,(2019) “Research in Medical Imaging Using Image
Processing Techniques,”Medical Imaging - Principles and Applications, IntechOpen, Edited by Y. Zhou,
Chapter 5.
[20] B.Selvapriya and B. Raghu,(2018) “A Color Map for Pseudo Color Processing of Medical Images,”
International Journal of Engineering & Technology, 7 (3.34) 954-958.
[21] K. Ansari, S. Moradian, and S.H. Amirshahi,(2005)” Ideal Compression of Reflectance Curves by
the use of Fundamental Color Stimuli”,10th Congress of the International Colour Association, AIC
Colour 2005 , Granada, Spain.
AUTHORS
Keivan Ansari received his Ph.D. in color engineering from Amirkabir University
(Polytechnic of Tehran),2005. He is an assistant professor in Color Imaging &Color
Image Processing research group in Color for Science & Technology Institute,
Tehran, IRAN. He is currently pursuing his postdoctoral at the ImViA laboratory in
the Bourgandy university, Dijon, FRANCE. His work has focused on the
development of Color Physics and its application in image processing.
Alexandre Krebs received the B.S., M.S. and Ph.D. in computer sciences and image
instrumentation from the University of Burgundy (France) in 2019. He is currently a
temporary lecturer and research assistant in the engineering school ESIREM in Dijon,
France. His research includes digestive endoscopy, Narrow Band Imaging,
multispectral imaging, stomach lesions, machine Learning, transfer learning, Inverse
problem, and optimization.
Yannick Benezeth is associate professor at the Univ. Bourgogne Franche-
Comté(France). He obtained his Ph.D. in computer science from the Univ. of Orléans
in 2009. He also received the engineering degree from the ENSI de Bourges and the
MS degree from the University of Versailles-Saint-Quentin-en-Yvelines in 2006. His
research interests include biomedical engineering, image processing, and video
analytics. Application areas include video health monitoring and endoscopy.

Franck Marzani received his M.Sc. in computer science from the University of
Rennes, France in 1989. He obtained his Ph.D. in computer vision and image
processing from the University of Burgundy, Dijon, France in 1998. He received
his “Habilitation à Diriger les Recherches” in 2007 and he is a full professor since
2009. he is currently the head of the ImViA research laboratory (Imaging &
Computer Vision) at the University of Burgundy. His research interests include
acquisition and analysis of images.

UNDERSTANDING HOW COLOUR CONTRAST IN HOTEL & TRAVEL
WEBSITE AFFECTS EMOTIONAL PERCEPTION, TRUST, AND
PURCHASE INTENTION OF VISITORS
Pimmanee Rattanawicha and Sutthipong Yungratog
Chulalongkorn Business School, Chulalongkorn University, Bangkok, Thailand
ABSTRACT
To understand how colour contrast in e-Commerce websites, such as hotel & travel websites, affects (1)
emotional perception (i.e. pleasant, arousal, and dominance), (2) trust, and (3) purchase intention of
visitors, a two-phase empirical study is conducted. In the first phase of this study, 120 volunteer
participants are asked to choose the most appropriate colour from a colour wheel for a hotel & travel
website. The colour “Blue Cyan”, the most chosen colour from this phase of study, is then used as the
foreground colour to develop three hotel & travel websites with three different colour contrast patterns for
the second phase of the study. A questionnaire is also developed from previous studies to collect
emotional perception, trust, and purchase intention data from another group of 145 volunteer participants.
It is found from data analysis that, for visitors as a whole, colour contrast has significant effects on their
purchase intention. For male visitors, colour contrast significantly affects their trust and purchase
intention. Moreover, for generation X and generation Z visitors, colour contrast has effects on their
emotional perception, trust, and purchase intention. However, no significant effect of colour contrast is
found in female or generation Y visitors.
KEYWORDS
Colour Contrast, e-Commerce, Website Design
9th
International Conference on Advances in Computing and Information Technology
(ACITY 2019) – http://airccse.org/csit/V9N17.html

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[28] Shapiro, A. G. 2008. Separating color from color contrast. Journal of Vision, 8(1), 1-18.
[29] Yungratog, S. & Rattanawicha, P. 2019. Effect of Color Contrast in e-Commerce Websites on
Emotional Perception, Trust, and Purchase Intention of Visitors: An Empirical Study Design. The 4th

International Conference on Innovative Education and Technology (ICIET 2019), 209-213.
[30] Zhou, X., & Lin, Y. 2015. The Study on the Influence Mechanism of Website Features on Consumer
Purchase Intention. 8th International Symposium on Computational Intelligence and Design, 104-107.
AUTHORS
Pimmanee Rattanawicha is an assistant professor at Chulalongkorn Business School, Chulalongkorn
University, Bangkok, Thailand. Her research interests include e-Commerce, HCI, UX/UI design.
Sutthipong Yungratog got his Master’s degree in IT in Business from Chulalongkorn Business School,
Chulalongkorn University, Bangkok, Thailand. He is now planning for his PhD study in HCI.

NONNEGATIVE MATRIX FACTORIZATION UNDER ADVERSARIAL
NOISE
Peter Ballen
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA
ABSTRACT
Nonnegative Matrix Factorization (NMF) is a popular tool to estimate the missing entries of a dataset
under the assumption that the true data has a low-dimensional factorization. One example of such a
matrix is found in movie recommendation settings, where NMF corresponds to predicting how a user
would rate a movie. Traditional NMF algorithms assume the input data is generated from the underlying
representation plus mean-zero independent Gaussian noise. However, this simplistic assumption does not
hold in real-world settings that contain more complex or adversarial noise. We provide a new NMF
algorithm that is more robust towards these nonstandard noise patterns. Our algorithm outperforms
existing algorithms on movie rating datasets, where adversarial noise corresponds to a group of
adversarial users attempting to review-bomb a movie.
KEYWORDS
Nonnegative Matrix Factorization, Matrix Completion, Recommendation, Adversarial Noise, Outlier
Detection, Linear Model
5th
International Conference on Data Mining and Applications (DMAP 2019) –
http://airccse.org/csit/V9N16.html

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AUTHORS
Peter Ballen is a PhD student at the University of Pennsylvania, where he studies matrix factorization
algorithms, their theoretical properties, and their applications in data mining.

PROPOSING A HYBRID APPROACH FOR EMOTION
CLASSIFICATION USING AUDIO AND VIDEO DATA
Reza Rafeh1
, Rezvan Azimi Khojasteh2
, Naji Alobaidi3
1
Centre for Information Technology, Waikato Institute of Technology, Hamilton, New Zealand
2
Department of Computer Engineering, Malayer Branch, Islamic Azad University, Hamedan, Iran
3
Department of Computer Engineering, Unitec Institute of Technology, Auckland, New Zealand
ABSTRACT
Emotion recognition has been a research topic in the field of Human Computer Interaction (HCI) during
recent years. Computers have become an inseparable part of human life. Users need human-like
interaction to better communicate with computers. Many researchers have become interested in emotion
recognition and classification using different sources. A hybrid approach of audio and text has been
recently introduced. All such approaches have been done to raise the accuracy and appropriateness of
emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion
recognition. The innovation of this approach is selecting the characteristics of audio and video and their
features as a unique specification for classification. In this research, the SVM method has been used for
classifying the data in the SAVEE database. The experimental results show the maximum classification
accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is
99.26%.
KEYWORDS
Emotion Classification, Emotions Analysis, Emotion Detection, SVM, Speech Emotion Recognition;
5th
International Conference on Computer Science and Information Technology (CSTY
2019) – http://airccse.org/csit/V9N14.html

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AUTHORS
Reza Rafeh is a senior lecturer at Waikato Institute of Technology. He received his
PhD in computer science from Monash University. His research areas cover data
mining, big data and analytics, recommender systems, software engineering and
modelling, constraint programming, and health informatics.
Rezvan Azimi Khojasteh received her MSc in Software Engineering from Islamic
Azad University, Malayer Branch. Her research area includes emotion mining and
data analytics.
Naji Alobaidi received his MSc in Computer Science from Unitec Institute of
Technology. His research areas cover data analytics, emotion mining, and vehicular
adhoc networks.

A FACIAL RECOGNITION-BASED VIDEO ENCRYPTION APPROACH
TO PREVENT FAKEDEEP VIDEOS
Alex Liang1
, Yu Su2
and Fangyan Zhang3
1
St. Margaret's Episcopal School, San Juan Capistrano, CA 92675
2
Department of Computer Science, California State Polytechnic University, Pomona, CA, 91768
3
ASML, San Jose, CA, 95131
ABSTRACT
Deepfake is a kind of technique which forges video with a certain purpose. It is in urgent demand that one
approach can defect if a video is deepfaked or not. It also can reduce a video to be exposed to slanderous
deepfakes and content theft. This paper proposes a useful tool which can encrypt and verify a video
through proper corresponding algorithms and defect it accurately. Experiment in the paper shows that the
tool has realized our goal and we can put it into practice.
KEYWORDS
Video Encryption, Video Verification, Encryption Algorithm, Decryption algorithm
6th
International Conference on Computer Science, Engineering and Information
Technology (CSEIT-2019) - http://airccse.org/csit/V9N13.html

REFERENCES
[1] D. Güera and E. J. Delp, "Deepfake Video Detection Using Recurrent Neural Networks," 2018 15th
IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland,
New Zealand, 2018, pp. 1-6.
[2] Exposing DeepFake Videos By Detecting Face Warping Artifacts Yuezun Li, Siwei Lyu Computer
Science Department University at Albany, State University of New York, USA
[3] Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news
detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
(pp. 797-806). ACM.
[4] Polletta, F., & Callahan, J. (2019). Deep stories, nostalgia narratives, and fake news: Storytelling in
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[5] Singhania, S., Fernandez, N., & Rao, S. (2017, November). 3han: A deep neural network for fake
news detection. In International Conference on Neural Information Processing (pp. 572-581). Springer,
Cham.
[6] Güera, D., & Delp, E. J. (2018, November). Deepfake video detection using recurrent neural
networks. In 2018 15th IEEE International Conference on Advanced Video and Signal Based
Surveillance (AVSS) (pp. 1-6). IEEE.
[7] Citron, D. K., & Chesney, R. (2018). Deep Fakes: A Looming Crisis for National Security,
Democracy and Privacy?. Lawfare.
[8] Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. "
O'Reilly Media, Inc.".
[9] Pulli, K., Baksheev, A., Kornyakov, K., & Eruhimov, V. (2012). Real-time computer vision with
OpenCV. Communications of the ACM, 55(6), 61-69.
[10] Li, Y., & Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts. arXiv
preprint arXiv:1811.00656, 2.
[11] Cozzolino, D., Thies, J., Rössler, A., Riess, C., Nießner, M., & Verdoliva, L. (2018).
Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv preprint
arXiv:1812.02510.
[12] Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019). The Deepfake Detection
Challenge (DFDC) Preview Dataset. arXiv preprint arXiv:1910.08854.

AN IMAGE CLASSIFICATION-BASED APPROACH TO AUTOMATE
VIDEO PLAYING DETECTION AT SYSTEM LEVEL
Eric Liu1
, Samuel Walcoff2
, Qi Lu3
and Yu Sun4
1
Aracadia High School, Arcadia, CA, 92697
2
Department of Computer Science, University of California, Santa Cruz Santa Cruz, CA 95064
3
Department of Social Science, University of California, Irvine Irvine, CA, 92697
4
Department of Computer Science, California State Polytechnic University, Pomona, CA, 91768
ABSTRACT
Tech distraction has become a critical issue on people’s work and study productivity, particularly with the
growing amount of digital content from the social media site such as Youtube. Although browser-based
plug-ins are available to help block and monitor the sites, they do not work for all scenarios. In this paper,
we present a system-level video playing detection engine that captures screenshots and analyze the
screenshot image using deep learning, in order to predict whether the image has videos in it or not. A
mobile app has also been developed to enable parents to control the video playing detection remotely.
KEYWORDS
Machine learning, Tech distraction, Image classification
8th
International Conference on Natural Language Processing (NLP 2019) -

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AUTOMATIC EXTRACTION OF FEATURE LINES ON 3D SURFACE
Zhihong Mao, Ruichao Wang and Yulin Zhou
Division of Intelligent Manufacturing, Wuyi University, Jiangmen529020, China
ABSTRACT
Many applications in mesh processing require the detection of feature lines. Feature lines convey the
inherent features of the shape. Existing techniques to find feature lines in discrete surfaces are relied on
user-specified thresholds, inaccurate and time-consuming. We use an automatic approximation technique
to estimate the optimal threshold for detecting feature lines. Some examples are presented to show our
method is effective, which leads to improve the feature lines visualization.
KEY WORDS
Feature Lines; Extraction; Meshes
9th
International Conference on Computer Science, Engineering and Applications (CCSEA
2019) - http://airccse.org/csit/V9N09.html

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Crest Lines on Meshes”, Symposium on Solid and Physical Modeling’05, pp227-232.
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A Modified MLS Approximation”, Computer-Aided Design, Vol. 38, No.2, pp173-180.
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meshes”, Int. J. of Engineering science and Innovative Technology, Vol. 2, No. 3, pp330-339.
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[16] Lee, Y., Markosian, L., Lee, S., & Hughes, J. F, (2007) ”Line drawings via abstracted shading”,
ACM Transactions on Graphics, Vol. 26, No. 3, pp1-9.
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A SURVEY OF STATE-OF-THE-ART GANBASED APPROACHES TO
IMAGE SYNTHESIS
Shirin Nasr Esfahani1
and Shahram Latifi2
1
Department of Computer Science, UNLV, Las Vegas, USA
2
Department of Electrical & Computer Eng., UNLV, Las Vegas, USA
ABSTRACT
In the past few years, Generative Adversarial Networks (GANs) have received immense attention by
researchers in a variety of application domains. This new field of deep learning has been growing rapidly
and has provided a way to learn deep representations without extensive use of annotated training data.
Their achievements may be used in a variety of applications, including speech synthesis, image and video
generation, semantic image editing, and style transfer. Image synthesis is an important component of
expert systems and it attracted much attention since the introduction of GANs. However, GANs are
known to be difficult to train especially when they try to generate high resolution images. This paper
gives a thorough overview of the state-of-the-art GANs-based approaches in four applicable areas of
image generation including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Aging, and 3D
Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to
traditional approaches in the fields of image processing and machine vision.
KEYWORDS
Conditional generative adversarial networks (cGANs), image synthesis, image-to-image translation, text-
to-image synthesis, 3D GANs.
9th

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AUTHORS
Shirin Nasr Esfahani received her M.S. degree in computer science – scientific
computation from Sharif University of technology, Tehran- Iran. She is currently a
Ph.D. candidate in computer science, University of Nevada, Las Vegas (UNLV). Her
fields of interest include, hyper spectral image processing, neural networks, deep
learning and data mining.
Shahram Latifireceived the Master of Science and the PhD degrees both in Electri cal
and Computer Engineering from Louisiana State University, Baton Rouge, in 1986
and 1989, respectively. He is currently a Professor of Electrical Engineering at the
University of Nevada, Las Vegas.

BLIND IMAGE QUALITY ASSESSMENT USING SINGULAR VALUE
DECOMPOSITION BASED DOMINANT EIGENVECTORS FOR
FEATURE SELECTION
Besma Sadou1
, Atidel Lahoulou2*
, Toufik Bouden1
, Anderson R. Avila3
, Tiago H. Falk3
, Zahid
Akhtar4
1
Non Destructive Testing Laboratory, University of Jijel, Algeria
2
LAOTI laboratory, University of Jijel, Algeria
3
Institut National de la Recherche Scientifique, University of Québec, Montreal, Canada 4University of
Memphis, USA
ABSTRACT
In this paper, a new no-reference image quality assessment (NR-IQA) metric for grey images is proposed
using LIVE II image database. The features used are extracted from three well-known NR-IQA objective
metrics based on natural scene statistical attributes from three different domains. These metrics may
contain redundant, noisy or less informative features which affect the quality score prediction. In order to
overcome this drawback, the first step of our work consists in selecting the most relevant image quality
features by using Singular Value Decomposition (SVD) based dominant eigenvectors. The second step is
performed by employing Relevance Vector Machine (RVM) to learn the mapping between the previously
selected features and human opinion scores. Simulations demonstrate that the proposed metric performs
very well in terms of correlation and monotonicity.
KEYWORDS
Natural Scene Statistics (NSS), Singular Value Decomposition (SVD), dominant eigenvectors, Relevance
Vector Machine (RVM).
9th

REFERENCES
[1] D. Zhang, Y. Ding , N. Zheng, “Nature scene statistics approach based on ICA for no- reference
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assessment:http://www.its.bldrdoc.gov/vqeg/projects/frtv_phaseI/
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National Academy of Sciences, February 2009.
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AUTHORS
Besma Sadou is currently a PhD student in the department of Electronics at university of Jijel (Algeria).
She also works as full-time teacher of mathematics at the middle school. Her research interests are
focused on reduced and no-reference image quality assessment.
Atidel Lahoulou is Doctor in Signals and Images from Sorbonne Paris Cité (France) since 2012. She
earned her Habilitation Universitaire in 2017 and is currently associate professor in the department of
computer science at university of Jijel (Algeria). Her research interests include visual data quality
evaluation and enhancement, biometrics, machine learning and cybersecurity.
Toufik Bouden received the engineer diploma (1992), MSc (1995) and PhD (2007) degrees in automatics
and signal processing from Electronics Institute of Annaba University (Algeria). Since 2015, he is full
professor in the department of Automatics. His areas of research are signal and image processing,
nondestructive testing and materials characterization, biometrics, transmission security and watermarking,
chaos, fractional system analysis, synthesis and control.
Anderson R. Avila received his B.Sc. in Computer Science from Federal University of Sao Carlos, Brazil,
in 2004 and his M.Sc in Information Engineering from Federal University of ABC in 2014. In October
2013, Anderson worked as a short-term visiting researcher at INRS, where he now pursues his Ph.D
degree on the topic of speaker and emotion recognition. His research interests include pattern recognition
and multimodal signal processing applied to biometrics.
Tiago H. Falk is an Associate Professor at INRS-EMT, University of Quebec and Director of the
Multimedia Signal Analysis and Enhancement (MuSAE) Lab. His research interests are in multimedia
quality measurement and enhancement, with a particular focus on human-inspired technologies.

Zahid Akhtar is a research assistant professor at the University of Memphis (USA). Prior to joining the
University of Memphis, he was a postdoctoral fellow at INRS-EMT-University of Quebec (Canada),
University of Udine (Italy), Bahcesehir University (Turkey), and University of Cagliari (Italy),
respectively. Dr. Akhtar received a PhD in electronic and computer engineering from the University of
Cagliari (Italy). His research interests are biometrics, affect recognition, multimedia quality assessment,
and cybersecurity.

VULNERABILITY ANALYSIS OF IP CAMERAS USING ARP
POISONING
Thomas Doughty1
, Nauman Israr2
and Usman Adeel3
1
BSc (Hons) Cyber Security and Networks, Teesside University, Middlesbrough, UK
2
Senior Lecturer in Networks and Communication, Teesside University, Middlesbrough, UK
3
Senior Lecturer in Computer Science, Teesside University, Middlesbrough, UK
ABSTRACT
Internet Protocol (IP) cameras and Internet of Things (IoT) devices are known for their vulnerabilities,
and Man in the Middle attacks present a significant privacy and security concern. Because the attacks are
easy to perform and highly effective, this allows attackers to steal information and disrupt access to
services. We evaluate the security of six IP cameras by performing and outlining various attacks which
can be used by criminals. A threat scenario is used to describe how a criminal may attack cameras before
and during a burglary. Our findings show that IP cameras remain vulnerable to ARP Poisoning or
Spoofing, and while some cameras use Digest Authentication to obfuscate passwords, some vendors and
applications remain insecure. We suggest methods to prevent ARP Poisoning, and reiterate the need for
good password policy.
KEYWORDS
Security, Camera, Internet of Things, Passwords, Sniffing, Authentication
8th
International Conference on Soft Computing, Artificial Intelligence and Applications
(SAI 2019) - http://airccse.org/csit/V9N07.html

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AUTHORS
Thomas Doughty is a graduate of Teesside University and received a BSc (Hon) in
Cybersecurity and Networks. His research interests include Cyber Securit y and the
Internet of Things.
Dr. Nauman Israr is currently a Senior Lecturer in Networks and Communication at
Teesside University. His research interests are include Wireless Sensor Networks,
Intelligent Computing and Cluster Communication.
Dr. Usman Adeel is currently a Senior Lecturer in Computer Science at Teesside
University. He holds a PhD in Computing from Imperial College, London. His research
interests are focused on Distributed Sensing Systems and their applications for Internet
of Things, Cyber-physical Systems

BRAIN COMPUTER INTERFACE FOR BIOMETRIC
AUTHENTICATION BY RECORDING SIGNAL
Abd Abrahim Mosslah1
. Reyadh Hazim Mahdi2
and Shokhan M. AlBarzinji3
1
University of Anbar, College of Islamic Science, Anbar- Iraq
2
University of Mustansiriyah, Dept. of ComputerScience,College of Science, Baghdad,Iraq
3
College of Computer Science and Information Technology, University of Anbar
ABSTRACT
Electroencephalogram(EEG) is done in several ways, which are referred to as brainwaves, which
scientists interpret as an electromagnetic phenomenon that reflects the activity in the human brain, this
study is used to diagnose brain diseases such as schizophrenia, epilepsy, Parkinson's, Alzheimer's, etc. It
is also used in brain machine interfaces and in brain computers. In these applications wireless recording is
necessary for these waves. What we need today is Authentication? Authentication is obtained from
several techniques, in this paper we will check the efficiency of these techniques such as password and
pin. There are also biometrics techniques used to obtain authentication such as heart rate, fingerprint, eye
mesh and sound, these techniques give acceptable authentication. If we want to get a technology that
gives us integrated and efficient authentication, we use brain wave recording. The aim of the technique in
our proposed paper is to improve the efficiency of the reception of radio waves in the brain and to provide
authentication.
KEYWORD
Related work, EEG brain signal, Brain wave, Overall projcet outline, System requirements.
6th
International Conference on Artificial Intelligence and Applications (AIAP-2019) -

REFERENCE
[1] Electroencephalogram.PDF,15 July 2007. 
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283–286.

[15] C. He and Z. J. Wang, “An independent component analysis (ICA) based approach for EEG person
authentication,” in Proc. 3rd ICBBE, 2010, pp. 1–10.
[16] A. Riera, A. Soria-Frisch, M. Caparrini, C. Grau, and G. Ruffini, “Unobtrusive biometric system
based on electroencephalogram analysis,” EURASIP J. Adv. Signal Process, vol. 2008, 2008.
[17] F. Su, H. Zhou, Z. Feng, and J. Ma, “A biometric-based covert warning system using EEG,” in Proc.
5th IAPR Int. Conf. Biometrics ICB, 2012, pp. 342–347.
[18] P. Campisi et al., “Brain waves based user recognition using the ‘eyes closed resting conditions’
protocol,” in Proc. IEEE Int. WIFS, Nov. 2011, pp. 1–6,
[19] D. La Rocca, P. Campisi, and G. Scarano, “On the repeatability of EEG features in a biometric
recognition framework using a resting state protocol,” in Proc. BIOSIGNALS, 2013, pp. 20–2.
[20] R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles, “The electroencephalogram as a
biometric,” in Proc. Can. Conf. Electr. Comput. EngComput., 2001, pp. 1363–1366.
[21] K. Das, S. Zhang, B. Giesbrecht, And M. P. Eckstein, “Using Rapid Visually Evoked Eeg Activity
For Person İdentification,” İ 2493.
AUTHORS
M.Sc. Abd Abrahim Mosslh. and who was born in the Alaesawi village, Fallujah, in
1971. obtained his M.Sc. in Comput Mustanseriah. Iraq Baghdad. He is currently
instructor of Islamic Universi ty of Anbar Iraq. His research interests are Artificial
Neural Networks, Computer Networks, Image Processing, Software Engineering, and
Genet
M.Sc. Reyadh Hazim Mahdiobtained utara / Malaysia. He is currently instructor of
College of Science University of Mustanseriah IRAQ-BAGHDAD His research
interests are Artificial Neural Netwo Computer Networks, Image Processing, and
Software Engineering
M.SC Shokhan M. Al-Barzinji. University of Anbar. Iraq anbar. She is currently
instructor of College of Computer Science and Information Technogy, University of
Anbar, Anbar, Iraq. His re search interests are Medical Image processing, Image
processing, Internet of Things, cloud computing and visualization

METHOD FOR THE DETECTION OF CARRIERIN-CARRIER SIGNALS
BASED ON FOURTHORDER CUMULANTS
Vasyl Semenov1
, Pavel Omelchenko1
and Oleh Kruhlyk1
1
Department of Algorithms, Delta SPE LLC, Kiev, Ukraine
ABSTRACT
The method for the detection of Carrier-in-Carrier signals based on the calculation of fourthorder
cumulants is proposed. In accordance with the methodology based on the “Area under the curve” (AUC)
parameter, a threshold value for the decision rule is established. It was found that the proposed method
provides the correct detection of the sum of QPSK signals for a wide range of signal-to-noise ratios. The
obtained AUC value indicates the high efficiency of the proposed detection method. The advantage of the
proposed detection method over the “radiuses” method is also shown.
KEYWORDS
Carrier-in-Carrier, Cumulants, QPSK.
7th
International Conference on Computational Science and Engineering (CSE) -

REFERENCES
[1] Agne, Craig & Cornell, Billy & Dale, Mark & Keams, Ronald & Lee, Frank, (2010) “Sharedspectrum
bandwidth efficient satellite communications”, Proceedings of the IEEE Military Communications
Conference (MILCOM' 10), pp341-346.
[2] Gouldieff, Vincent & Palicot, Jacques, (2015) “MISO Estimation of Asynchronously Mixed BPSK
Sources”, Proc. IEEE Conf. EUSIPCO, pp369-373.
[3] Feng, Hao & Gao, Yong, (2016) “High-Speed Parallel Particle Filter for PCMA Signal Blind
Separation”, Radioelectronics and Communications Systems, Vol.59, No.10, pp305-313.
[4] Semenov, Vasyl, (2018) “Method of Iterative Single-Channel Blind Separation for QPSK Signals”,
Mathematical and computer modelling, Vol. 17, No. 2, pp108-116.
[5] Fernandes, Carlos Estevao R. & Comon, Pierre & Favier, Gerard, (2010) “Blind identification of
MISO-FIR channels”, Signal Processing, Vol. 90, pp490–503.
[6] Swami, Anantharam & and Sadler, Brain M., (2000) “Hierarchical digital modulation classification
using cumulants,” IEEE Trans. Commun., Vol. 48, pp416-429.
[7] Wunderlich, Adam & Goossens, Bart & Abbey, Craig K. “Optimal Joint Detection and Estimation
That Maximizes ROC-Type Curves” (2016) IEEE Transactions on Medical Imaging, Vol. 35, No.9,
pp2164– 2173.
AUTHORS
Vasyl Semenov received a Ph.D. in Acoustics from Institute of Hydromechanics of
National Academy of Sciences of Ukraine in 2004. He is currently the chief of the
Department of Algorithms at Delta SPE LLC, Kiev, Ukraine and doctoral student at
the Institute of Cybernetics of National Academy of Sciences of Ukraine. His main
research interests are in the fields of digital signal processing, demodulation, blind
separation, and recognition systems.
Pavel Omelchenko received a Ph.D. in Mathematics from Institute of Mathematics of National Academy
of Sciences of Ukraine in 2010. He is currently the member of the Department of Algorithms at Delta
SPE LLC, Kiev, Ukraine. His main research interests are in the fields of digital signal processing,
demodulation, blind separation, and cryptanalysis systems.
Oleh Kruhlyk received M.Sc. degree in Radioelectronics from the National Technical University of
Ukraine “Kiev Polytechnic Institute” in 2017. He is currently the member of the Department of
Algorithms at Delta SPE LLC, Kiev, Ukraine and Ph.D. student at the National Technical University of
Ukraine “Kiev Polytechnic Institute”. His main research interests are in the fields of digital signal
processing, demodulation, and blind separation and methods.

A DFG PROCESSOR IMPLEMENTATION FOR DIGITAL SIGNAL
PROCESSING APPLICATIONS
Ali Shatnawi, Osama Al-Khaleel and Hala Alzoubi
Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan
ABSTRACT
This paper proposes a new scheduling technique for digital signal processing (DSP) applications
represented by data flow graphs (DFGs). Hardware implementation in the form of a specialized
embedded system, is proposed. The scheduling technique achieves the optimal schedule of a given DFG
at design time. The optimality criterion targeted in the proposed algorithm is the maximum throughput
than can be achieved by the available hardware resources. Each task is presented in a form of an
instruction to be executed on the available hardware. The architecture is composed of one or multiple
homogeneous pipelined processing elements, designed to achieve the maximum possible sampling rate
for several DSP applications. In this paper, we present a processor implementation of the proposed
architecture. It comprises one processing element where all tasks are processed sequentially. The
hardware components are built on an FPGA chip using Verilog HDL. The architecture requires a very
small area size, which is represented by the number of slice registers and the number of slice lookup
tables (LUTs). The proposed scheduling technique is shown to outperform the retiming technique, which
is proposed in the literature, by 19.3%.
KEYWORDS
Data Flow Graphs, Task Scheduling, Processor Design, Hardware Description Language
8th
International Conference on Advanced Computer Science and Information Technology
(ICAIT 2019) - http://airccse.org/csit/V9N04.html

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AUTHORS
Ali Shatnawi is a professor of computer engineering. He received the B.Sc and M.Sc
in electrical and computer engineering from the Jordan University of Science and
Technology (JUST) in 1989 and 1992, respectively; and the Ph.D degree in electrical
and computer engineering from Concordia University, Canada, in 1996. He has been
on the faculty of the Jordan University of Science and Technology since 1996. He
served as the director of computer centre 1996-1999, Vice-dean 2001-2002, Dean of
IT at Hashemite University 2002-2005 and dean of Computer and Information Technology, JUST, 2016-
2018. His present research includes algorithms and optimizations, hardware scheduling, computer
architecture and high level synthesis of DSP applications.
Osama Al-Khaleel is an associate professor of Computer Engineering in the
Department of Computer Engineering of Jordan University of Science and
Technology (Irbid, Jordan), received his B.S in Electrical Engineering from Jordan
University of Science and Technology in 1999, M.Sc. and Ph.D. in Computer
Engineering from Case Western Reserve University, Cleveland, OH, USA in 2003
and 2006 respectively. Currently, his main research interests are in embedded systems
design, reconfigurable computing, computer arithmetic, and logic design.
Hala AL-Zu'bi received her B.SC. in Computer Engineering from Yarmouk University
in 2012, and M.Sc. in Computer Engineering from Jordan University of Science &
Technology in 2018. Her research interests include computer architecture, hardware
description language, task scheduling and data flow computing.

OCCLUSION HANDLED BLOCK-BASED STEREO MATCHING WITH
IMAGE SEGMENTATION
Jisu Kim, Cheolhyeong Park, Ju O Kim and Deokwoo Lee
Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea
ABSTRACT
This paper chiefly deals with techniques of stereo vision, particularly focuses on the procedure of stereo
matching. In addition, the proposed approach deals with detection of the regions of occlusion. Prior to
carrying out stereo matching, image segmentation is conducted in order to achieve precise matching
results. In practice, in stereo vision, matching algorithm sometimes suffers from insufficient accuracy if
occlusion is inherent with the scene of interest. Searching the matching regions is conducted based on
cross correlation and based on finding a region of the minimum mean square error of the difference
between the areas of interest defined in matching window. Middlebury dataset is used for experiments,
comparison with the existed results, and the proposed algorithm shows better performance than the
existed matching algorithms. To evaluate the proposed algorithm, we compare the result of disparity to
the existed ones.
KEYWORDS
Occlusion, Stereo vision, Segmentation, Matching.
Full Text : https://airccj.org/CSCP/vol9/csit90303.pdf
7th
International Conference on Signal Image Processing and Multimedia (SIPM 2019) -

REFERENCES
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Dense Disparity Maps from Color Stereo Images, an Efficient Implementation”, International Journal of
Computer Vision, Vol. 47, No. 1, pp.79-88.
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International Journal of Computer Vision, Vol. 120, No. 3, pp.256-271.
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PatchMatch and Ground Control Points Constraint”, Advances in Multimedia Information Processing –
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Mean-Shift Tracking Algorithm”, Proceedings of Annual Conference of IEIE, pp.704-706.
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corresponding algorithms”, International Journal of Computer Vision, Vol. 47, No. 1, pp.7-42.

[15] Scharstein, D & Szeliski, R, (2003) “High-Accuracy Stereo Depth Maps Using Structured Light”,
IIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.195- 202.
AUTHOR
Jisu Kim is in department of computer engineering, Keimyung University, Daegu,
Republic of Korea. He is currently working on image processing, computer vision,
signal processing and machine learning. He is currently pursuing his M.S degree in
computer engineering.
Cheolhyeong Park is in department of computer engineering, Keimyung University,
Daegu, Republic of Korea. He is currently working on geometric image analysis,
computer vision, computer graphics and machine learning. He is in the course of
integrated B.S and M.S degree in computer engineering.
Ju O Kim is in department of computer engineering, Keimyung University, Daegu,
Republic of Korea. He is currently working on image analysis and Processing. He is
pursuing B.S degree in computer engineering.
Dr. Deokwoo Lee is an Assistant Professor in the department of computer engineering at Keimyung
University. Dr. Lee has received B.S degree in electrical engineering from Kyungpook
National University, Daegu, Republic of Korea, and M.S and Ph.D degree from North
Carolina State University, Raleigh, NC, USA, respectively. He has been working on
the areas of computer vision, image processing, signal processing and machine
learning. In particular, he has been conducting camera calibration, bio-signal analysis
and image denoising.

ORDER PRESERVING STREAM PROCESSING IN FOG COMPUTING
ARCHITECTURES
Vidyasankar
Department of Computer Science, Memorial University of Newfoundland, St. John’s, Newfoundland,
Canada
ABSTRACT
A Fog Computing architecture consists of edge nodes that generate and possibly pre-process (sensor)
data, fog nodes that do some processing quickly and do any actuations that may be needed, and cloud
nodes that may perform further detailed analysis for long-term and archival purposes. Processing of a
batch of input data is distributed into sub-computations which are executed at the different nodes of the
architecture. In many applications, the computations are expected to preserve the order in which the
batches arrive at the sources. In this paper, we discuss mechanisms for performing the computations at a
node in correct order, by storing some batches temporarily and/or dropping some batches. The former
option causes a delay in processing and the latter option affects Quality of Service (QoS). We bring out
the tradeoffsbetween processing delay and storage capabilities of the nodes, and also between QoS and
the storage capabilities.
KEYWORDS
Fog computing, Order preserving computations, Quality of Service
Full Text : https://airccj.org/CSCP/vol9/csit90104.pdf
3rd
International Conference on Computer Science and Information Technology (COMIT

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Association for Computing Machinery.
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Twenty Seventh International Conference on Software Engineering and Data Engineering (SEDE18).
[8] N. Conway (2008)“Transactions and data stream processing”, Online Publication, pages 1–28.
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[16] storm.apache.org/releases/1.0.6/Transactional-topologies.html.
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