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image Cryptography based on the Imitation of Gene Fusion and Horizontal Gene Transfer 7

Image Cryptography based on the Imitation of Gene Fusion and...

引用

7th International conference on image processing Theory, Tools and applications (IPTA)

作者： Hamici, Zoubir Al Zaytoonah Univ Jordan Dept Elect Engn Signal Proc ZUJ Amman Jordan

ISBN: (纸本)9781538618424

In this paper a novel signal processing algorithm for secure image communication is proposed. It is a genetic algorithm that combines both gene fusion and Horizontal Gene Transfer (HGT) inspired from the spread of antibiotic resistance in bacteria. The symmetric encryption key playing the role of a pathogen is generated by controlled multi-layer random sequences. The principal key is then absorbed by the organism genome represented by the image. The process of encryption starts by a full uptake of the key-agent or pathogen which has the size of the whole image;the image and the principal key are merged together in a gene fusion process. A second phase of encryption is an obfuscation process produced by an HGT where the genes are pixels and the chromosomes are the rows and columns of the image. The obfuscation process as a second layer of encryption is realized by two sub-keys from which the principal key matrix is constructed. The whole process is repeated recursively L rounds. A Salt extracted from the image hash-value is used against chosen-plaintext cryptanalysis, therefore, even a modification of one pixel will generate different encryption keys adding a stealthy-key feature to the cipher. The key generation process with the two layers of encryption of the genetic algorithm are fully described. Results of the signal processing algorithm based on gene fusion and HGT show a great promise in genetic inspired data security.

关键词： cryptography genetic algorithm horizontal gene transfer gene fusion obfuscation

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Transfer Learning For Diabetic Macular Edema (DME) Detection On Optical Coherence Tomography (OCT) images 5

Transfer Learning For Diabetic Macular Edema (DME) Detection...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Chan, Genevieve C. Y. Muhammad, Awais Shah, Syed A. A. Tang, Tong B. Lu, Cheng-Kai Meriaudeau, Fabrice Univ Teknol PETRONAS Ctr Intelligent Signal & Imaging Res Dept Elect & Elect Engn Bandar Seri Iskandar 32610 Perak Darul Rid Malaysia

ISBN: (纸本)9781509055593

Diabetic Macular Edema (DME) is a common eye disease that causes irreversible vision loss for diabetic patients, if left untreated. Thus, early diagnosis of DME could help in early treatment and prevent blindness. This paper aims to create a framework based on deep learning for DME recognition on Spectral Domain Optical Coherence Tomography (SD-OCT) images through transfer learning. First, images are preprocessed: denoised using Block-Matching and 3-Dimension (BM3D) filtering and cropped through image boundary extraction. Later, features are extracted using CNN of AlexNet and finally images are classified using SVM classifier. The results are evaluated using 8-fold cross-validation. The experiments show that denoised and cropped images lead to better classification performances, exceeding previous other recent published works of 96% accuracy.

关键词： Diabetic Macular Edema (DME) Convolutional Neural Network (CNN) Spectral Domain Optical Coherence Tomography (SD-OCT)

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signal Noise Filter Structure Selection for Predictive PI Controller in a Wireless Networked Control Setting 5

Signal Noise Filter Structure Selection for Predictive PI Co...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Hassan, Sabo Miya Ibrahim, Rosdiazli Saad, Nordin Asirvadam, Vijanth Sagayan Bingi, Kishore Chung, Tran Duc Univ Teknol PETRONAS Dept Elect & Elect Engn Perak 32610 Malaysia Abubakar Tafawa Balewa Univ Dept Elect & Elect Engn PMB 0248 Bauchi Nigeria

ISBN: (纸本)9781509055593

The predictive proportional integral (PPI) controller being simple and non conservative like its Smith predictor and internal model control (IMC) counterparts, has the ability to be employed in environments characterised by uncertainties such as stochastic network delays. However, its performance degrades in the presence of measurement noise. This is due to the prediction involve in the controller which acts like the derivative term of PID controller that amplifies high frequency noise. This necessitated the need for additional filtering of the noisy signals. Therefore, this work proposes three several filter configurations for PPI controller in a wireless environment. Simulation results shows that placing the filter within the PPI controller gives smooth control signal that can yield better control performance compared to both feedback and cascade filter configurations.

关键词： Filter ISA wireless PPI controller signal noise wireless networked control Wireless HART

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Content-based Fauna image Retrieval System 5

Content-based Fauna Image Retrieval System

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Mustaffa, Mas Rina San, Wong San Univ Putra Malaysia Fac Comp Sci & Informat Technol Multimedia Dept Upm Serdang 43400 Selangor Darul Malaysia

ISBN: (纸本)9781509055593

Many animal species exist in this world and there are always new species being discovered each year. Therefore, it is very important that these valuable species be documented properly to be referred to in future. Numerous information retrieval systems for managing and documenting animal species today only allow users to search animal images and descriptions online via text-based input. Therefore, people without knowledge on the animal species or without Internet access are not able to search using the systems. Motivated by these issues, the focus of this work is to construct a colour-shape content-based image representation for fauna. Two orders of the Colour Moment are used to represent the colour feature while the K-means approach is used to represent the shape feature. Based on the conducted quantitative and qualitative studies, the proposed fusion method together with the Content-based image Retrieval (CBIR) system are found to be very effective in retrieving animal images similar to the given query, able to provide reliable and useful information on animal species, an easy system to interact with, and has easy to understand and user-friendly interfaces.

关键词： Animals Content-based image Retrieval Colour Moments K-means

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OKWW Filter for Poisson Noise Removal in Low-Light Condition Digital image 5

OKWW Filter for Poisson Noise Removal in Low-Light Condition...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Sari, S. Chia, Y. Y. Mohd, M. N. H. Taujuddin, N. S. A. M. Ibrahim, N. Roslan, H. Univ Tun Hussein Onn Malaysia Fac Elect & Elect Engn Johor Baharu Malaysia Univ Tun Hussein Onn Malaysia Fac Engn Technol Johor Baharu Malaysia

ISBN: (纸本)9781509055593

Due to the limitation of camera technologies in low cost development, digital images are easily corrupted by various types of noise such as Salt and Pepper noise, Gaussian noise and Poisson noise. For digital image captured in the photon limited low light condition, the effect of image noise especially Poisson noise will be more obvious, degrading the quality of the image. Thus, this study aims to develop new denoising techniques for Poisson noise removal in low light condition digital images. This study proposed a method which is referred to the OKWW Filter which utilizes Otsu Threshold, Kuwahara Filter, Wiener Filter, and Wavelet Threshold. This filter is designed for high Poisson noise removal. The proposed filter performance is compared with other existing denoising techniques. The results show that proposed OKWW Filter is the best in high level Poisson noise removal while preserving the edges and fine details of noisy images.

关键词： Denoising Low light Poisson noise Threshold

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Remote Sensing images Classification Using Moment Features and Attribute Profiles 5

Remote Sensing Images Classification Using Moment Features a...

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IEEE International conference on signal and image processing applications (ICSIPA)

作者： Roochi, Niloofar Ghasemi Ghassemian, Hassan Mirzapour, Fardin Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781509055593

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. Remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At the present work, we use the moments and Attribute Morphology Profiles (APs) to extract texture information from satellite panchromatic images. We use four conventional moments in pattern recognition such as Geometric, Chebyshev, Legendre and Zernike moments and APs to extract features from remote sensing image. An MP is constructed based on the repeated use of openings and closings by reconstruction of a structuring elements (SE) of an increasing size, applied to a scalar image. Then, we use those two set of features together. The well-known support vector machine (SVM) is used for supervised classification. We compare our proposed method with moments and APs. Different criteria such as average accuracy, overall accuracy, kappa statistic and computation time are used for assessment of classification performance.

关键词： Moment Chebyshev Geometric Legendre Zernike Attribute Morphology Profile classification support vector machine (SVM) remote sensing

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Speech Recognition using Facial sEMG 5

Speech Recognition using Facial sEMG

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Soon, Mok Win Anuar, Muhammad Ikmal Hanafi Abidin, Mohamad Hafizat Zainal Azaman, Ahmad Syukri Noor, Norliza Mohd Malaysia Japan Int Inst Technol Kuala Lumpur Malaysia Univ Teknol Malaysia UTM Razak Sch Engn & Adv Technol Skudai Malaysia

ISBN: (纸本)9781509055593

This paper presents a study of speech recognition based on electromyographic biosignals captured from the articulatory muscles in the face using surface electrodes. This paper compares the speech recognition system for spoken English and Malay words by a group of Malay native speakers. Feature extraction was done in both temporal and time-frequency domains. Temporal features used are integrated EMG (IEMG), mean absolute value (MAV), root mean square (RMS), variance (VAR), standard deviation (SD), and simple square integral (SSI) where time-frequency domain features were obtained using discrete wavelet transform. For classification, random forest classifier and ANNs multilayer perceptron both gave the overall best performance on using temporal features and timefrequency features respectively. The result of the classification shows that the Malay language is can be used in sEMG speech recognition.

关键词： surface electromyography speech recognition biosignal feature extraction EMG signal

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SAR image Time Series Analysis in Vegetation Classification Using Global and Local Information Based Linear Discriminant Analysis 25

SAR Image Time Series Analysis in Vegetation Classification ...

引用

25th signal processing and Communications applications conference (SIU)

作者： Sakarya, Ufuk Demirpolat, Caner TUBITAK UZAY TR-06800 Ankara Turkey

ISBN: (纸本)9781509064946

Vegetation classification using SAR images is one of the research topics in remote sensing. In this paper, SAR image time series analysis in vegetation classification using global and local information based linear discriminant analysis is presented. It is experimentally demonstrated that the use of local pattern information in addition to global pattern information has increased both accuracy and time performance in vegetation classification using time-series TerraSAR-x images.

关键词： linear discriminant analysis SAR image time series dimension reduction

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Classification of Stress into Emotional, Mental, Physical and No Stress Using Electroencephalogram signal Analysis 5

Classification of Stress into Emotional, Mental, Physical an...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Berbano, Adrian Erniell U. Pengson, Hanz Niccole V. Razon, Cedric Gerard V. Tungcul, Kristel Chloe G. Prado, Seigfred V. Univ Santo Tomas Dept Elect Engn Manila Philippines

ISBN: (纸本)9781509055593

The paper presents further research on neural engineering that focuses on the classification of emotional, mental, physical and no stress through the use of Electroencephalography (EEG) signal analysis. Stress is one of the leading causes of several health-related problems and diseases. Therefore, it becomes necessary for people to monitor their stress. The human body acquires and responds to stress in different ways resulting to two classifications of stress namely, mental and emotional stress. Traditional methods in classifying stress such as through questionnaires and self-assessment tests are said to be subjective since they rely on personal judgment. Thus, in this study, stress is classified through an objective measure which is EEG signal analysis. The features of the EEG recordings are then pre-processed, extracted, and selected using Discrete Wavelet Transform (DWT). These features are then ussed as inputs to classify stress using Artificial Neural Network (ANN) and validated using K-fold Cross Validation Method. Lastly, the results from the software assisted method is compared to the results of the traditional method.

关键词： Electroencephalogram Discrete Wavelet Transform Artificial Neural Network K - fold Cross Validation Mental Stress Emotional Stress Physical Stress No Stress

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Multi-modal Data Fusion For Pain Intensity Assessment and Classification 7

Multi-modal Data Fusion For Pain Intensity Assessment and Cl...

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7th International conference on image processing Theory, Tools and applications (IPTA)

作者： Thiam, Patrick Schwenker, Friedhelm Ulm Univ Inst Neural Informat Proc Ulm Germany

ISBN: (纸本)9781538618424

In this work, an assessment of several fusion architectures is undertaken within the scope of the development of a pain intensity classification system. The assessment is based on the recently recorded SenseEmotion Database [1], which consists of several individuals subjected to three gradually increasing levels of pain intensity, induced through temperature elevation (heat stimulation) under controlled conditions. Several modalities, including audio, video, respiration, electrocardiography, electromyography and electrodermal activity, were synchronously recorded during the experiments. A broad spectrum of descriptors is extracted from each of the involved modalities, followed by an assessment of the combination of the extracted descriptors through several fusion architectures. Experimental validation suggests that the choice of an appropriate fusion architecture, which is able to significantly improve over the performance of the best single modality, mainly depends on the amount of data available for the training of the classification architecture.

关键词： Multiple Classifier Systems signal processing Pain Intensity Estimation

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