Khaled Tarmissi

@psu.edu.sa

Department of Computer Sciences/College of Computer and Information Science
Prince Sultan University

Khaled Tarmissi


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https://researchid.co/khaledtarmissi

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science Applications, Computational Theory and Mathematics, Artificial Intelligence
39

Scopus Publications

Scopus Publications

  • Adaptive Modeling of Positional Falsification Malicious Behavior Using Type-2 Fuzzy-Based Aggregated Trust Analysis
    Khaled Tarmissi, Amal Alshardan, Nuha Alruwais, Mohammed Alahmadi, Adel Albshri, Rakan Alanazi, Hanadi Alkhudhayr, Haitham Assiri
    Transactions on Emerging Telecommunications Technologies, 2026
    Internet of Vehicles (IoV) involves a number of connected vehicles that can continuously share Basic Safety Messages (BSM). These messages contain details such as vehicle position, speed, and travel direction to improve traffic flow, but they also carry risks, including false positional attacks that can lead to unsafe driving decisions. Traditional methods in this domain primarily focus on privacy, identity verification, and fixed rules for detecting abnormal behavior. Due to this inability, they cannot fully capture precise vehicular motion. Also, it struggles to adapt to high motion vehicles, GPS noise, and hidden attack patterns. To address these limitations, this paper proposes TrustFRF (Trust‐aware Fuzzy Random Forest), an adaptive vehicular trust‐based detection model that continuously assesses vehicle trust based on its position consistency, motion patterns, and past‐time interactions, without relying on predefined attack patterns. The proposed model is evaluated using the publicly available “VANET malicious node dataset.” Experimental results suggest that TrustFRF achieves a notable detection accuracy of 99.40% with a low inference latency of 0.53 s for real‐time edge implementation.
  • Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images
    Khaled Tarmissi, Jamal Alsamri, Mashael Maashi, Mashael M. Asiri, Abdulsamad Ebrahim Yahya, Abdulwhab Alkharashi, Monir Abdullah, Marwa Obayya
    Scientific Reports, 2025
    Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body's blood cells and maintaining the overall immune and health system. Red and yellow BM are the two various kinds of BM. A comprehensive identification of these cells assists in the primary and precise recognition of these disorders. The recognition and identification of BM cells are crucial bases for haematology diagnostics. Physical study of BM detection and classification presently performed in medical laboratories can be primarily insufficient owing to various factors, such as prolonged and challenging. Recently, with the fast growth of deep learning (DL) and machine learning (ML) methods, object detection methods have been progressively used for cell detection. DL is a secondary domain of artificial intelligence (AI) methods able to spontaneously assess delicate graphical features to create exact predictions that have been newly popularized in various imaging-related tasks. This study proposes a Multimodal Transfer Learning with Snake Optimization on Bone Marrow Cell Classification (MTLSO-BMCC) technique using biomedical histopathological images. The main intention of the MTLSO-BMCC technique is to identify and classify BM cells utilizing HI. To achieve this, the presented MTLSO-BMCC method initially performs image preprocessing using a median filter (MF) for noise removal. Besides, the multimodal feature extraction process is accomplished in InceptionV3, Deep SqueezeNet, and SE-DenseNet models. The presented MTLSO-BMCC technique employs the hybrid kernel extreme learning machine (HKELM) method for the BM classification method. Finally, the snake optimization algorithm (SOA) is implemented to tune the parameter of the HKELM model. A widespread MTLSO-BMCC methodology simulation is accomplished under the BM Cell Classification dataset. The experimental validation of the MTLSO-BMCC methodology portrayed a superior accuracy value of 98.60% over existing approaches.
  • Vulnerability Analysis and Quality Improvement of Early Wildfire Detection Datasets for Machine-Learning Applications
    Atef Shalan, Khaled Tarmissi, Nafeeul Alam Walee
    Proceedings 2024 13th IEEE International Conference on Communication Systems and Network Technologies Csnt 2024, 2024
    Wildfires are recognized as highly devastating natural disasters, capable of causing irreversible harm to the environment, structures, and human lives and the aftermath of this dreadful occurrence often involves exorbitant expenses for repairs. Detecting wildfires poses a significant challenge, but identifying scenarios for early detection could empower cities and countries to proactive prepare strategies for wildfire management. The primary objective of this research paper is to provide analysis and improvement of the major vulnerabilities of existing public datasets for early wildfire detection using machine learning. We discuss the current weaknesses in these datasets and illustrate their impacts on machine learning solutions. We then describe the required dataset quality aspects and discuss their advantages for early wildfire detection using machine learning. A sample quality dataset and a sample deep learning model using TensorFlow are made publicly available through GitHub.
  • Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles
    Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi
    Aims Mathematics, 2024
    <p>Autonomous vehicles (AVs), particularly self-driving cars, have produced a large amount of interest in artificial intelligence (AI), intelligent transportation, and computer vision. Tracing and detecting numerous targets in real-time, mainly in city arrangements in adversarial environmental conditions, has become a significant challenge for AVs. The effectiveness of vehicle detection has been measured as a crucial stage in intelligent visual surveillance or traffic monitoring. After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. However, according to statistical learning, conventional AI is fundamental, facing restrictions due to manual feature engineering and restricted flexibility in adaptive environments. This study presents the explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection for autonomous vehicles (XAIFTL-AWCDAV) method. The XAIFTL-AWCDAV model's main aim is to detect and classify weather conditions for AVs in challenging scenarios. In the preprocessing stage, the XAIFTL-AWCDAV model utilizes a non-local mean filtering (NLM) method for noise reduction. Besides, the XAIFTL-AWCDAV model performs feature extraction by fusing three models: EfficientNet, SqueezeNet, and MobileNetv2. The denoising autoencoder (DAE) technique is employed to classify adverse weather conditions. Next, the DAE method's hyperparameter selection uses the Levy sooty tern optimization (LSTO) approach. Finally, to ensure the transparency of the model's predictions, XAIFTL-AWCDAV integrates explainable AI (XAI) techniques, utilizing SHAP to visualize and interpret each feature's impact on the model's decision-making process. The efficiency of the XAIFTL-AWCDAV method is validated by comprehensive studies using a benchmark dataset. Numerical results show that the XAIFTL-AWCDAV method obtained a superior value of 98.90% over recent techniques.</p>
  • Automated Attendance Taking System using Face Recognition
    Khalid Tarmissi, Hamad Allaaboun, Omar Abouellil, Saif Alharbi, Mohammed Soqati
    21st International Learning and Technology Conference Reality and Science Fiction in Education L and T 2024, 2024
    The process of attendance taking, whether in a classroom or in a job meeting has been taking a considerable amount of time from the teaching or meeting activity itself, especially with the large number of people attending them, in modern society, people require an advanced solution to this time-consuming activity, one that is available to everyone, anywhere at any time. In this paper the proposed solution is an application(Mutabe) that helps mainly academic faculty members take attendance from their phone using a single picture of all their classroom’s students, this image is then processed using advanced artificial intelligence face detection and recognition algorithms, with pre-trained models to give the instructor an easier way of taking attendance, the subsequentially generated attendance sheet is securely saved within a database, which can only be accessed by that instructor whether from their phone or from the associated website.
  • Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment
    Faisal S. Alsubaei, Haya Mesfer Alshahrani, Khaled Tarmissi, Abdelwahed Motwakel
    Intelligent Automation and Soft Computing, 2023
    Cybersecurity has become the most significant research area in the domain of the Internet of Things (IoT) owing to the ever-increasing number of cyberattacks. The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process. Furthermore, Android malware is increasing on a daily basis. So, precise malware detection analytical techniques need a large number of hardware resources that are significantly resource-limited for mobile devices. In this research article, an optimal Graph Convolutional Neural Network-based Malware Detection and classification (OGCNN-MDC) model is introduced for an IoT-cloud environment. The proposed OGCNN-MDC model aims to recognize and categorize malware occurrences in IoT-enabled cloud platforms. The presented OGCNN-MDC model has three stages in total, such as data pre-processing, malware detection and parameter tuning. To detect and classify the malware, the GCNN model is exploited in this work. In order to enhance the overall efficiency of the GCNN model, the Group Mean-based Optimizer (GMBO) algorithm is utilized to appropriately adjust the GCNN parameters, and this phenomenon shows the novelty of the current study. A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model. A comprehensive comparison study was conducted, and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.
  • Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model
    Hala J. Alshahrani, Khaled Tarmissi, Ayman Yafoz, Abdullah Mohamed, Manar Ahmed Hamza, Ishfaq Yaseen, Abu Sarwar Zamani, Mohammad Mahzari
    Intelligent Automation and Soft Computing, 2023
    The term ‘executed linguistics’ corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights. The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users. The Automatic Text Summarization (ATS) process reduces the primary size of the text without losing any basic components of the data. The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning (ALTS-MLODL) model. The presented ALTS-MLODL technique aims to summarize the text documents in the English language. To accomplish this objective, the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features. Next, the MLO algorithm is used for the effectual selection of the extracted features. For the text summarization process, the Cascaded Recurrent Neural Network (CRNN) model is exploited whereas the Whale Optimization Algorithm (WOA) is used as a hyperparameter optimizer. The exploitation of the MLO-based feature selection and the WOA-based hyperparameter tuning enhanced the summarization results. To validate the performance of the ALTS-MLODL technique, numerous simulation analyses were conducted. The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.
  • Dart Games Optimizer with Deep Learning-Based Computational Linguistics Named Entity Recognition
    Mesfer Al Duhayyim, Hala J. Alshahrani, Khaled Tarmissi, Heyam H. Al-Baity, Abdullah Mohamed, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohamed I. Eldesouki
    Intelligent Automation and Soft Computing, 2023
    Computational linguistics is an engineering-based scientific discipline. It deals with understanding written and spoken language from a computational viewpoint. Further, the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting. Named Entity Recognition (NER) is a fundamental task in the data extraction process. It concentrates on identifying and labelling the atomic components from several texts grouped under different entities, such as organizations, people, places, and times. Further, the NER mechanism identifies and removes more types of entities as per the requirements. The significance of the NER mechanism has been well-established in Natural Language Processing (NLP) tasks, and various research investigations have been conducted to develop novel NER methods. The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning (ML) techniques to Deep Learning (DL) techniques. In this aspect, the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics (DGOHDL-CL) model for NER. The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities. In the presented DGOHDL-CL technique, the word embedding process is executed at the initial stage with the help of the word2vec model. For the NER mechanism, the Convolutional Gated Recurrent Unit (CGRU) model is employed in this work. At last, the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes. No earlier studies integrated the DGO mechanism with the CGRU model for NER. To exhibit the superiority of the proposed DGOHDL-CL technique, a widespread simulation analysis was executed on two datasets, CoNLL-2003 and OntoNotes 5.0. The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
  • Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus
    Hala J. Alshahrani, Abdulkhaleq Q. A. Hassan, Khaled Tarmissi, Amal S. Mehanna, Abdelwahed Motwakel, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohamed I. Eldesouki
    Computers Materials and Continua, 2023
    Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively.
  • Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment
    Abdelwahed Motwakel, Fadwa Alrowais, Khaled Tarmissi, Radwa Marzouk, Abdullah Mohamed, Abu Sarwar Zamani, Ishfaq Yaseen, Mohamed I. Eldesouki
    Intelligent Automation and Soft Computing, 2023
    The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such cyberattacks. With this motivation, the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection (ECSADL-CAD) model for the Software-Defined Networking (SDN)-enabled IoT environment. The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT environment. To attain this, the ECSADL-CAD model initially pre-processes the data. In the presented ECSADL-CAD model, the Reinforced Deep Belief Network (RDBN) model is employed for attack detection. At last, the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes. A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model. The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.
  • Improved Fruitf ly Optimization with Stacked Residual Deep Learning Based Email Classification
    Hala J. Alshahrani, Khaled Tarmissi, Ayman Yafoz, Abdullah Mohamed, Abdelwahed Motwakel, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohammad Mahzari
    Intelligent Automation and Soft Computing, 2023
  • Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus
    Manar Ahmed Hamza, Hala J. Alshahrani, Khaled Tarmissi, Ayman Yafoz, Amal S. Mehanna, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohamed I. Eldesouki
    Computer Systems Science and Engineering, 2023
  • Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection
    Manar Ahmed Hamza, Hala J. Alshahrani, Khaled Tarmissi, Ayman Yafoz, Amira Sayed A. Aziz, Mohammad Mahzari, Abu Sarwar Zamani, Ishfaq Yaseen
    Computer Systems Science and Engineering, 2023
  • Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization
    Abdelwahed Motwakel
    Computer Systems Science and Engineering, 2023
  • Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets
    Abdelwahed Motwakel, Hala J. Alshahrani, Abdulkhaleq Q. A. Hassan, Khaled Tarmissi, Amal S. Mehanna, Ishfaq Yaseen, Amgad Atta Abdelmageed, Mohammad Mahzari
    Computers Materials and Continua, 2023
  • Modelling of Metaheuristics with Machine Learning-Enabled Cybersecurity in Unmanned Aerial Vehicles
    Mohammed Rizwanullah, Hanan Abdullah Mengash, Mohammad Alamgeer, Khaled Tarmissi, Amira Sayed A. Aziz, Amgad Atta Abdelmageed, Mohamed Ibrahim Alsaid, Mohamed I. Eldesouki
    Sustainability Switzerland, 2022
  • Joint Power Control and Phase Shift Design for Future PD-NOMA IRS-Assisted Drone Communications under Imperfect SIC Decoding
    Saddam Aziz, Muhammad Irshad, Kallekh Afef, Heba G. Mohamed, Najm Alotaibi, Khaled Tarmissi, Mrim M. Alnfiai, Manar Ahmed Hamza
    Sensors, 2022
  • Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time
    Mubashar Sarfraz, Haya Mesfer Alshahrani, Khaled Tarmissi, Hussain Alshahrani, Mohamed Ahmed Elfaki, Manar Ahmed Hamza, Ali Nauman, Tahir Khurshaid
    Sensors, 2022
  • Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities
    Mesfer Al Duhayyim, Eatedal Alabdulkreem, Khaled Tarmissi, Mohammed Aljebreen, Bothaina Samih Ismail Abou El Khier, Abu Sarwar Zamani, Ishfaq Yaseen, Mohamed I. Eldesouki
    Applied Sciences Switzerland, 2022
  • Dwarf Mongoose Optimization with Machine-Learning-Driven Ransomware Detection in Internet of Things Environment
    Khalid A. Alissa, Dalia H. Elkamchouchi, Khaled Tarmissi, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Abdullah Mohamed, Mesfer Al Duhayyim
    Applied Sciences Switzerland, 2022
  • Planet Optimization with Deep Convolutional Neural Network for Lightweight Intrusion Detection in Resource-Constrained IoT Networks
    Khalid A. Alissa, Fatma S. Alrayes, Khaled Tarmissi, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Mahmoud Othman, Abdelwahed Motwakel
    Applied Sciences Switzerland, 2022
  • Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding
    Hala J. Alshahrani, Khaled Tarmissi, Hussain Alshahrani, Mohamed Ahmed Elfaki, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Manar Ahmed Hamza
    Applied Sciences Switzerland, 2022
  • A Robust Image Watermarking Technique Using Multi-DW-DCT Scheme
    Khaled Tarmissi, Atef Shalan, Ameera A. AlShareef, Saud S. Alotaibi
    Proceedings 2022 IEEE Acis 7th International Conference on Big Data Cloud Computing and Data Science Bcd 2022, 2022
  • UQU GIS-based Navigation System
    Shoaib Shahzad Obaidi, Khalid Tarmissi, Atef Shalan, Saud S. Alotaibi
    Proceedings 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2022, 2022
  • Automating the Extraction and Activation of Web Service Credibility Measures - A Framework Design
    Atef Shalan, Walaa Abo Elenin, Khaled Tarmissi, Alexander Boyett, Christopher Kadlec
    Proceedings 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2022, 2022
  • A Literature Review of Bitcoin Network Infrastructure, Methodology, and Challenges∗
    Khaled Tarmissi, Atef Shalan, Rayan Alsulamy, Sultan Almotiri, Abdulbaset Gaddah
    Proceedings of 2022 5th National Conference of Saudi Computers Colleges Nccc 2022, 2022
  • Protecting Digital Currency Transactions on Bitcoin Networks Against Delay-Based Attacks∗
    Khaled Tarmissi, Atef Shalan, Abdullah Al Shahrani, Abdulbaset Gaddah, Saud S. Alotaibi
    Proceedings of 2022 5th National Conference of Saudi Computers Colleges Nccc 2022, 2022
  • Mitigating Security Threats of Bitcoin Network by Reducing Message Broadcasts During Transaction Dissemination
    Khaled Tarmissi, Atef Shalan, Abdullah Al Shahrani, Rayan Alsulamy, Saud S. Alotaibi, Sarah Al-Shareef
    Proceedings 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2022, 2022
  • 6G Mobile Communications Systems: Requirements, Specifications, Challenges, Applications, and Technologies
    Sliman KA. A. Yaklaf, Khaled S. Tarmissi, Nasar Aldian Ambark Shashoa
    2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering Mi Sta 2021 Proceedings, 2021
  • Anti-malware efficiency evaluation framework
    Badr Alharbi, Hamdan Alzahrani, Ahmed Asseri, Khaled Taramisi
    2020 2nd International Conference on Computer and Information Sciences Iccis 2020, 2020
  • Towards secure password protection in portable applications
    Hossain Shahriar, Atef Shalan, Khaled Tarmissi
    Advances in Intelligent Systems and Computing, 2020
  • Mobile anti-phishing: Approaches and challenges
    Hossain Shahriar, Chi Zhang, Stephen Dunn, Robert Bronte, Atef Sahlan, Khaled Tarmissi
    Information Security Journal, 2019
  • Multi-Carrier Modulation Techniques for Light Fidelity Technology
    Sliman KA. A. Yaklaf, Khaled S. Tarmissi
    19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering Sta 2019, 2019
  • Data Protection Labware for Mobile Security
    Hossain Shahriar, Md Arabin Talukder, Hongmei Chi, Mohammad Rahman, Sheikh Ahamed, Atef Shalan, Khaled Tarmissi
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
  • Multivariate kernel diffusion for surface denoising
    Khaled Tarmissi, A. Ben Hamza
    Signal Image and Video Processing, 2011
  • Geometric mesh denoising via multivariate kernel diffusion
    Khaled Tarmissi, A. Ben Hamza
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
  • Information-theoretic hashing of 3D objects using spectral graph theory
    Khaled Tarmissi, A. Ben Hamza
    Expert Systems with Applications, 2009
  • Feature-preserving kernel diffusion for surface denoising
    Khaled Tarmissi, A. Ben Hamza
    Proceedings International Conference on Image Processing Icip, 2009
  • Statistical mesh distributions for 3D object topology
    Malik Qasaimeh, Ying Zhang, Khaled Tarmissi, A. Ben Hamza
    2007 9th International Symposium on Signal Processing and Its Applications Isspa 2007 Proceedings, 2007