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مجال
التميز
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تميز دراسي و بحثي
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البحوث المنشورة
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البحث (1):
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عنوان البحث:
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Developing an Ultra Wideband
Indoor Navigation System for Visually Impaired People
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رابط إلى البحث:
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Click
Here
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تاريخ النشر:
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22/07/2016
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موجز عن البحث:
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The number of individuals who
suffer from visual impairment is increasing rapidly. The most significant
barrier to improving the lives of the blind and visually impaired people is
their inability to navigate independently and safely. Indoor navigation systems
for blind and visually impaired people aim to guide them in navigating
independently in familiar and unfamiliar environments. Our system aims to
provide an assistive technology for blind and visually impaired individuals
by exploiting popular existing technologies that are often used by blind
individuals, such as the smartphone. The system provides users with guidance
statements that help them navigate from their current positions to desired
destinations. The system consists of four main components: mapping,
positioning, navigation, and interface. In order to implement these
components, three main applications need to be developed: application in
localization server, application in communication server, and application in
smartphone, each of which is located in a different place but connected to
the others. Functionalities test and blind test were conducted to evaluate
the system. The system proved its ability to aid blind individuals
effectively.
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البحث (2):
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عنوان البحث:
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Ultra wideband indoor
positioning technologies: Analysis and recent advances
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رابط إلى البحث:
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Click Here
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تاريخ النشر:
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16/05/2016
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موجز عن البحث:
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In recent years, indoor
positioning has emerged as a critical function in many end-user applications;
including military, civilian, disaster relief and peacekeeping missions. In
comparison with outdoor environments, sensing location information in indoor
environments requires a higher precision and is a more challenging task in
part because various objects reflect and disperse signals. Ultra WideBand
(UWB) is an emerging technology in the field of indoor positioning that has
shown better performance compared to others. In order to set the stage for
this work, we provide a survey of the state-of-the-art technologies in indoor
positioning, followed by a detailed comparative analysis of UWB positioning
technologies. We also provide an analysis of strengths, weaknesses,
opportunities, and threats (SWOT) to analyze the present state of UWB
positioning technologies. While SWOT is not a quantitative approach, it helps
in assessing the real status and in revealing the potential of UWB
positioning to effectively address the indoor positioning problem. Unlike
previous studies, this paper presents new taxonomies, reviews some major
recent advances, and argues for further exploration by the research community
of this challenging problem space.
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المؤتمرات العلمية:
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المؤتمر (1):
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عنوان المؤتمر:
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8th annual international
ICT-BDCS 2017
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تاريخ الإنعقاد:
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21-22/08/2017
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مكان
الإنعقاد:
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Singapore.
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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QT: A Quality Testing Tool for Data-Intensive Applications
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ملخص المشاركة:
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We present the DICE Quality
Testing (QT) tool, a software for load injection and testing of
Data-Intensive Applications (DIAs). The load injection mechanism of the DICE
QT tool is designed for Apache Storm – a mature, stable and well-known Big
Data technology for stream-based applications – and Apache Kafka. The QT tool
consists of two modules. The first module is named QT-GEN and can generate
input workload similar, but nevertheless non-identical, to the real workload
data supplied to it, with prescribed rates for each type of message. The
QT-LIB module is a Java library that provides custom spouts for autonomic
workload injection into DIAs. The paper demonstrates the applicability of the
tool on two case studies.
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المؤتمر (2):
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عنوان المؤتمر:
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2nd ICISDM
2018
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تاريخ الإنعقاد:
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9-11/04/2018
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مكان
الإنعقاد:
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Florida,USA
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Do Words with Certain Part of
Speech Tags Improve the Performance of Arabic Text Classification?
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ملخص المشاركة:
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Feature extraction – the process
of choosing feature types that can represent and discriminate between dataset
topics – is one of the critical steps in text classification and varies with
the language of the texts. Different feature types have been proposed for
Arabic text classification, ranging from features based on word orthography
(single word and character and word N-grams) to features based on linguistic
analysis (roots, stems). To the best of our knowledge, little attention has
been paid to investigating the performance of Arabic text classification when
Part of Speech (POS) tagging information is used to extract features. In this
study, we used a corpus comprising 4900 newspaper texts distributed evenly
over seven topics to investigate the effect of using POS tag distribution and
words that belong to certain POS tags on Arabic text classification, namely
nouns, verbs and adjectives. For feature selection, feature representation
and text classification we used Chi-squared, Log-Weighted Term Frequency Inverse
Document Frequency with Cosine Normalization (LTC) and support vector machine
(SVM) respectively. We used four metrics, namely accuracy, precision, recall
and F-measure to measure classification performance. Experiment data suggest
that the words achieved the best classification performance when the number
of features was low; however, the classification performance can be
marginally increased when nouns, verbs and adjectives are used as features,
given that the number of features is increased.
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المؤتمر (3):
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عنوان المؤتمر:
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11th International Conference on the Quality of Information
and Communications Technology
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تاريخ الإنعقاد:
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04/09/2018
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مكان
الإنعقاد:
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Coimbra, Portugal
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Publication Preview Source A
Neural-Network Driven Methodology for Anomaly Detection in Apache Spark
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ملخص المشاركة:
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Different types of runtime
anomalies can affect the user-perceived performance of batch analytics
applications. These anomalies may arise from hardware and software faults, or
concurrently executing workloads issued by other tenants in resources such as
CPU, memory, disk, or network. Even though techniques exist in the literature
for anomaly detection from monitoring data, they are typically blackbox and
therefore do not fully exploit available information on the user tasks. We
here focus in particular on Spark-based workloads, in which the analytic
operations are applied to a resilient distributed dataset (RDD) that can be
described as a direct acyclic graph. We develop a neural network based
methodology for anomaly detection that is able to improve the accuracy of
anomaly detection based on knowledge of the RDD characteristics. Using
experiments against multiple workloads and anomaly types, we show that our
method improves over other types of classifiers as well as against blackbox
performance anomaly detection.
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