Imaging time series for the classification of EMI discharge sources

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira

Recognition and Classification of Ancient Dwellings based on Elastic Grid and GLCM

Rectangle algorithm is designed to extract ancient dwellings from village satellite images according to their pixel features and shape features. For these unrecognized objects, we need to distinguish them by further extracting texture features of them. In order to get standardized sample, three pre-process operations including rotating operation, scaling operation, and clipping operation are designed to unify their sizes and directions

Improving k-nn search and subspace clustering based on local intrinsic dimensionality

In several novel applications such as multimedia and recommender systems, data is often represented as object feature vectors in high-dimensional spaces. The high-dimensional data is always a challenge for state-of-the-art algorithms, because of the so-called curse of dimensionality . As the dimensionality increases, the discriminative ability of similarity measures diminishes to the point where many data analysis algorithms, such as similarity search and clustering, that depend on them lose their effectiveness. One way to handle this challenge is by selecting the most important features, which is essential for providing compact object representations as well as improving the overall search and clustering performance. Having compact feature vectors can further reduce the storage space and the computational complexity of search and learning tasks. Support-Weighted Intrinsic Dimensionality (support-weighted ID) is a new promising feature selection criterion that estimates the contribution of each feature to the overall intrinsic dimensionality. Support-weighted ID identifies relevant features locally for each object, and penalizes those features that have locally lower discriminative power as well as higher density. In fact, support-weighted ID measures the ability of each feature to locally discriminate between objects in the dataset. Based on support-weighted ID, this dissertation introduces three main research contributions: First, this dissertation proposes NNWID-Descent, a similarity graph construction method that utilizes the support-weighted ID criterion to identify and retain relevant features locally for each object and enhance the overall graph quality. Second, with the aim to improve the accuracy and performance of cluster analysis, this dissertation introduces k-LIDoids, a subspace clustering algorithm that extends the utility of support-weighted ID within a clustering framework in order to gradually select the subset of informative and important features per cluster. k-LIDoids is able to construct clusters together with finding a low dimensional subspace for each cluster. Finally, using the compact object and cluster representations from NNWID-Descent and k-LIDoids, this dissertation defines LID-Fingerprint, a new binary fingerprinting and multi-level indexing framework for the high-dimensional data. LID-Fingerprint can be used for hiding the information as a way of preventing passive adversaries as well as providing an efficient and secure similarity search and retrieval for the data stored on the cloud. When compared to other state-of-the-art algorithms, the good practical performance provides an evidence for the effectiveness of the proposed algorithms for the data in high-dimensional spaces

Roughness of molecular property landscapes and its impact on modellability

In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks.Comment: 17 pages, 6 figures, 2 tables (SI with 17 pages, 16 figures

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

User identification system for inked fingerprint pattern based on central moments

The use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71

FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION

This thesis consists of three parts: face localization, features selection and classification process. Three methods were proposed to locate the face region in the input image. Two of them based on pattern (template) Matching Approach, and the other based on clustering approach. Five datasets of faces namely: YALE database, MIT-CBCL database, Indian database, BioID database and Caltech database were used to evaluate the proposed methods. For the first method, the template image is prepared previously by using a set of faces. Later, the input image is enhanced by applying n-means kernel to decrease the image noise. Then Normalized Correlation (NC) is used to measure the correlation coefficients between the template image and the input image regions. For the second method, instead of using n-means kernel, an optimized metrics are used to measure the difference between the template image and the input image regions. In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. The above-mentioned three methods showed accuracy of localization between 98% and 100% comparing with the existed methods. In the second part of the thesis, Discrete Wavelet Transform (DWT) utilized to transform the input image into number of wavelet coefficients. Then, the coefficients of weak statistical energy less than certain threshold were removed, and resulted in decreasing the primary wavelet coefficients number up to 98% out of the total coefficients. Later, only 40% statistical features were extracted from the hight energy features by using the variance modified metric. During the experimental (ORL) Dataset was used to test the proposed statistical method. Finally, Cluster-K-Nearest Neighbor (C-K-NN) was proposed to classify the input face based on the training faces images. The results showed a significant improvement of 99.39% in the ORL dataset and 100% in the Face94 dataset classification accuracy. Moreover, a new metrics were introduced to quantify the exactness of classification and some errors of the classification can be corrected. All the above experiments were implemented in MATLAB environment