Jordan University of Science and Technology

About: Jordan University of Science and Technology is a education organization based out in Irbid, Irbid, Jordan. It is known for research contribution in the topics: Population & Health care. The organization has 7582 authors who have published 13166 publications receiving 298158 citations. The organization is also known as: JUST.

Seizure classification in EEG signals utilizing Hilbert-Huang transform

Abstract: Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

Using Porous Fins for Heat Transfer Enhancement

Abstract: This work introduces a novel method that enhances the heat transfer from a given surface by using porous fins. The thermal performance of porous fins is estimated and compared with that of the conventional solid fins. It is found that using porous fin of porosity ∈ may enhance the performance of an equal size conventional solid fin and, as a result, save 100 e percent of the fin material. The effect of different design and operating parameters on the porous fin thermal performance is investigated. Examples of these parameters are Ra number, Da number, and thermal conductivity ratio. It is found that more enhancement in the porous fin performance may be achieved as Ra increases especially at large Da numbers. Also, it is found that there is an optimum limit for the thermal conductivity ratio beyond which there is no further improvement in the fin performance.

CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments

Abstract: The proliferation of RF networks coupled with the diverse and growing set of mobile devices, opened the doors for a new class of context awareness through contact-free ambient sensing. Since our initial challenges paper in 2007, the field of device-free passive sensing has witnessed an exponential growth; covering areas such as intrusion detection, mobile healthcare, whole-home gesture recognition, traffic estimation, border protection, among others. In this talk, we give a holistic overview of the area of contact-free ambient sensing based on RF technology, highlighting how it evolved over a decade from binary-detection in controlled environments to commercial systems for border protection and smart homes. We also give insights about the current trends and possible future research challenges.

A machine learning approach to building domain-specific search engines

Abstract: Domain-specific search engines are becoming increasingly popular because they offer increased accuracy and extra features not possible with general, Web-wide search engines. Unfortunately, they are also difficult and time-consuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, text classification and information extraction that enables efficient spidering, populates topic hierarchies, and identifies informative text segments. Using these techniques, we have built a demonstration system: a search engine for computer science research papers available at www.cora.justrcsettrch.com.