Peperites: Insight into the Submarine Eruptions within Walash Volcanosedimentary Group, Mawat Area, Iraqi Kurdistan Region

Peperites are volcanosedimentary materials generated by the mingling of magma and unconsolidated wet sediments. They have unique insights into submarine volcanisms and the tectonic environments where they form. For the 1st time, the authors identified two types of peperites (blocky and fluidal) hosted by micritic limestone rocks in the Walash Volcanosedimentary Group of the Mawat area, Kurdistan Region-Iraq. They are designated as peperitic facies one and two (PF1 and PF2) and consist of black basaltic rocks mixed with chocolate-brown micritic limestone rocks. Their abundance demonstrates the contemporaneity of deep marine sediment deposition and submarine volcanism during Walash’s nascent arc. Despite hydrothermal alteration, the basaltic rocks retained their magmatic textures. Basaltic rocks comprise mainly albite, anorthite, diopside, hematite, and alkali-feldspar. Calcite dominates micritic limestone rocks, while quartz is minor. Based on geochemical data, igneous sections are basaltic rocks with tholeiitic series that are strongly enriched in Light Rare Earth Elements with low concentration ratios of (La/Yb) and (Sr/Y), indicating geochemical affinity to normal island arc basalt with a primitive arc signature. Furthermore, their formation is thought to be caused by partial melting of subducted slabs deep within 30 km and the associated derived fluids above the subducted slab. Thirteen species of planktonic foraminifera (Morozovella) are identified through paleontological research and biostratigraphy. Using these various tools lead the authors to illustrate the tectonic setting of the formation of peperitic rocks in arc fronts of the subducted Walash arc during the Middle to Late Paleocene (60 Ma)

Fingerprint Verification System Using Support Vector Machine

Efficient fingerprint verification system is needed in many places for personal identification to access physical facilities, information etc. This paper proposes robust verification system based on features extracted from human fingerprints and a pattern classifier called Support Vector Machine (SVM). Three set of features are fused together and passed to the classifier. The fused feature is used to train the system for effective verification of users fingerprint images. The result obtained after testing 100 fingerprints is very encouraging

Vertical Off-line Signature Feature Block for Verification

Handwritten signature image is normally used as a mark of endorsement of written document. Signatures of the same person vary and they can be forged by imposters. Effective feature extraction algorithm is needed in off-line signature verification. Robust features capable of increases interpersonal variation and decreases intra personal variation are required. This work presents robust signature feature that can be used to build effective off-line signature verification system. Signature processing is performed and the preprocessed signature image is vertically divided into sixteen smaller image blocks through the center of gravity. Three features are extracted from these smaller image blocks. Feature vector is formed and are passed to Support Vector Machine (SVM) for training and classification. The proposed signature feature vector increases the accuracy of tested off-line signature verification syste

Hand Image Feature for Human Identification

This paper presents an algorithm for efficient personal identification using robust hand features. The feature is extracted from hand boundary points and print of hand palm. The centre of gravity of the edge map of the hand image is determined to serve as a reference point. Thereafter City block distances between the reference point and hand boundary points are found. These distance feature vectors are compared using Euclidean distance measure for effective image classification. The proposed algorithm will improve personal identification in access control and attendance recor

Web based Real Time Water Pressure Monitoring System

Nowadays, researches on real-time monitoring system have developed greatly. This research is intended to develop a real-time water pressure monitoring system on water distribution networks. Pressure monitoring is very important in assuring the availability of water supply to the community. Therefore, it is reasonable to develop a web-based real-time water monitoring system which is more efficient and easier to use.This system utilized MPX5700AP as sensor to measure water pressure and Arduino UNO as system’s performance control center with GSM/GPRS Shield application enabling the system to communicate with the server through GSM/GPRS network. This system was designed by applying C Arduino, HTML, PHP, and SQL as programming languages.Result of the evaluations indicated system prototype operated well. System was able to measure water pressure and displaying graphics and data of the measurement on the webpage

Utilization of Landsat-8 data for the estimation of carrot and maize crop water footprint under the arid climate of Saudi Arabia

Understanding the spatial variability of Water Footprint (WF) of crops is essential for the efficient use of the available water resources. Therefore, this study was designed to bridge the gap in knowledge existed in the area of WF in the arid climate of Saudi Arabia by quantifying the remote sensing based blue-WF (WFblue) of maize and carrot crops cultivated during the period from December 2015 to December 2016. Agrometeorological (empirical) estimated WF components, namely, the WFblue, the green-WF (WFgreen) and the grey-WF (WFgrey), were determined at a farm scale in conjunction with the climatic conditions and cropping patterns. On the other hand, the WFBlue was estimated from Landsat-8 data using energy balance and yield models. The empirical approach based WFBlue was used as a reference for the accuracy assessment of the Landsat-8 estimated WFBlue. The empirically estimated WF of silage maize ranged from 3540 m3 t-1 to 4960 m3 t-1. Out of which the WFgreen, the WFblue and the WFgrey composed 0.74%, 83.28% and 15.98%, respectively. For the carrot crop; however, the WF ranged between 2970 m3 t-1 and 5020 m3 t-1. Where, the WFgreen, the WFblue and the WFgrey represented 0.50%, 77.31% and 22.19%, respectively. Using Landsat-8 data, the WFblue was found to vary across the crops from 2552 m3 t-1 (silage maize) to 3010 m3 t-1 (carrot). Results also revealed a highly significant linear relationship between the empirical and the Landsat-8 derived WFBlue (R2 = 0.77, P>F = 0.001). The utility of Landsat-8 data in mapping WF showed reliable seasonal estimates, which can greatly enhance precision management practices of irrigation water

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

Real-time event detection in field sport videos

This chapter describes a real-time system for event detection in sports broadcasts. The approach presented is applicable to a wide range of field sports. Using two independent event detection approaches that work simultaneously, the system is capable of accurately detecting scores, near misses, and other exciting parts of a game that do not result in a score. The results obtained across a diverse dataset of different field sports are promising, demonstrating over 90% accuracy for a feature-based event detector and 100% accuracy for a scoreboard-based detector detecting only score

Finite-Time Synchronization of the Rabinovich and Rabinovich-Fabrikant Chaotic Systems for Different Evolvable Parameters

This paper addresses the challenge of synchronizing the dynamics of two distinct 3D chaotic systems, specifically the Rabinovich and Rabinovich-Fabrikant systems, employing a finite-time synchronization approach. These chaotic systems exhibit diverse characteristics and evolving chaotic attractors, influenced by specific parameters and initial conditions. Our proposed low-cost finite-time synchronization method leverages the signum function's tracking properties to facilitate controlled coupling within a finite time frame. The design of finite-time control laws is rooted in Lyapunov stability criteria and lemmas. Numerical experiments conducted within the MATLAB simulation environment demonstrate the successful asymptotic synchronization of the master and slave systems within finite time. To assess the global robustness of our control scheme, we applied it across various system parameters and initial conditions. Remarkably, our results reveal consistent synchronization times and dynamics across these different scenarios. In summary, this study presents a finite-time synchronization solution for non-identical 3D chaotic systems, showcasing the potential for robust and reliable synchronization under varying conditions