Search

Your search keyword '"Nebhen, Jamel"' showing total 306 results
Start Over Author "Nebhen, Jamel"
306 results on '"Nebhen, Jamel"'

1. An Executable Formal Model of the VHDL in Isabelle/HOL

Author

Khan, Wilayat, Hou, Zhe, Sanan, David, Nebhen, Jamel, Liu, Yang, and Tiu, Alwen

Subjects
Computer Science - Computation and Language, Computer Science - Formal Languages and Automata Theory, Computer Science - Logic in Computer Science
Abstract

In the hardware design process, hardware components are usually described in a hardware description language. Most of the hardware description languages, such as Verilog and VHDL, do not have mathematical foundation and hence are not fit for formal reasoning about the design. To enable formal reasoning in one of the most commonly used description language VHDL, we define a formal model of the VHDL language in Isabelle/HOL. Our model targets the functional part of VHDL designs used in industry, specifically the design of the LEON3 processor's integer unit. We cover a wide range of features in the VHDL language that are usually not modelled in the literature and define a novel operational semantics for it. Furthermore, our model can be exported to OCaml code for execution, turning the formal model into a VHDL simulator. We have tested our simulator against simple designs used in the literature, as well as the div32 module in the LEON3 design. The Isabelle/HOL code is publicly available: https://zhehou.github.io/apps/VHDLModel.zip

Published
2022

2. An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Author

Umair, Muhammad Basit, Iqbal, Zeshan, Bilal, Muhammad, Almohamad, Tarik Adnan, Nebhen, Jamel, and Mehmood, Raja Majid

Subjects
Computer Science - Networking and Internet Architecture, 62M45, 68T07, C.2.3
Abstract

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique., Comment: 14 pages, 4 figures, 11 tables, Accepted for publication in CMC-Computers, Materials & Continua

Published
2021
Full Text
View/download PDF

22. A Low Power CMOS Variable True Random Number Generator for LDPC Decoders

Author

Nebhen, Jamel, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Goel, Nilesh, editor, Hasan, Shazia, editor, and Kalaichelvi, V., editor

Published
2020
Full Text
View/download PDF

25. High DC-Gain Two-Stage OTA Using Positive Feedback and Split-Length Transistor Techniques

Author

Nebhen, Jamel, Masmoudi, Mohamed, Rahajandraibe, Wenceslas, Aguir, Khalifa, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Alfaries, Auhood, editor, Mengash, Hanan, editor, Yasar, Ansar, editor, and Shakshuki, Elhadi, editor

Published
2019
Full Text
View/download PDF

29. Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

Author

Krishnendu, S., Bharath, B. N., Bhatia, Vimal, Nebhen, Jamel, Dobrovolny, Michal, and Ratnarajah, Tharmalingam

Abstract

The ever pervasive growth in information services and technology has resulted in the outbreak of demand for data in the wireless networks. This has made the network operators to ponder over the imminent difficulties, such as computing capabilities and fronthaul–backhaul link capacities. Hence, to bridge the gap between the cloud capacity and requirement of mobile services by the network edges, edge computing, and caching techniques have been gaining more and more attention from researchers across the world. Further, motivated by the successful applications of machine learning (ML) in solving complex and dynamic problems, in this article, it has been used to advance edge caching capabilities. The proposed ML-based algorithms have been evaluated and proved to have better performance compared with the existing conventional algorithms. The mean squared error (mse), for the proposed deep learning (DL) algorithm, is 20-times less than the existing algorithms and while comparing with the simple neural network, the gain in mse for the proposed DL algorithm is observed around 27%. Similarly for the federated learning-based caching algorithm, the average cache hit gain is of the order of $10^{4}$, hence demonstrating the benefit of the proposed algorithms. In addition, opportunities and challenges for a promising upcoming future of ML in edge computing and content popularity prediction have also been discussed.

Published
2024
Full Text
View/download PDF

30. AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia

Author

Ghayvat, Hemant, Awais, Muhammad, Bashir, AK, Pandya, Sharnil, Zuhair, Mohd, Rashid, Mamoon, Nebhen, Jamel, Ghayvat, Hemant, Awais, Muhammad, Bashir, AK, Pandya, Sharnil, Zuhair, Mohd, Rashid, Mamoon, and Nebhen, Jamel

Abstract

A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.

Published
2023

39. Low Noise CMOS Chopper Amplifier for MEMS Gas Sensor

Author

Nebhen, Jamel, Meillère, Stéphane, Masmoudi, Mohamed, Seguin, Jean-Luc, Aguir, Khalifa, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Kamel, Mohamed, editor, Karray, Fakhri, editor, Gueaieb, Wail, editor, and Khamis, Alaa, editor

Published
2011
Full Text
View/download PDF

50. Reconfigurable Pattern Patch Antenna for Mid-Band 5G: A Review

Author

Rahena Isa, Siti, primary, Jusoh, Muzammil, additional, Sabapathy, Thennarasan, additional, Nebhen, Jamel, additional, Ramlee Kamarudin, Muhammad, additional, Nasrun Osman, Mohamed, additional, Hussain Abbasi, Qammer, additional, A. Rahim, Hasliza, additional, Najib Mohd Yasin, Mohd, additional, and Jack Soh, Ping, additional

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results