Optical Mark Recognition (OMR) is the technology of electronically extracting intended data from marked fields, such as squareand bubbles fields, on printed forms. OMR technology is particularly useful for applications in which large numbers of hand-filled forms need to be processed quickly and with a great degree of accuracy. The technique is particularly popular with schools and universities for the reading in of multiple choice exam papers. This paper proposed OMRbased on Modify Multi-Connect Architecture (MMCA) associative memory, its work in two phases: training phase and recognition phase. The proposed method was also able to detect more than one or no selected choice. Among 800 test samples with 8 types of grid answer sheets and total 58000 questions, the system exhibits an accuracy is 99.96% in the recognition of marked, thus making it suitable for real world applications.
In this work, the surface of the telescope’s mirror is cleaned using an atmospheric-pressure radio frequency plasma jet (APRFPJ), which is generated by Argon gas between two coaxial metal electrodes. The RF power supply is set to 2 MHz frequencies with three different power levels: 20, 50, and 80 W. Carbon, that has adhered to the surface, can be effectively removed using the plasma cleaning technique, which also modifies any residual bonds. The cleaned surface was clearly distinguished using an optical emission spectroscopy (OES) technique and a water contact angle (WCA) analyzer for the activation property on their surfaces. The sample showed a super hydrophilic surface at an angle of 1° after 2.5 minutes of plasma tre
... Show MoreMost Internet of Vehicles (IoV) applications are delay-sensitive and require resources for data storage and tasks processing, which is very difficult to afford by vehicles. Such tasks are often offloaded to more powerful entities, like cloud and fog servers. Fog computing is decentralized infrastructure located between data source and cloud, supplies several benefits that make it a non-frivolous extension of the cloud. The high volume data which is generated by vehicles’ sensors and also the limited computation capabilities of vehicles have imposed several challenges on VANETs systems. Therefore, VANETs is integrated with fog computing to form a paradigm namely Vehicular Fog Computing (VFC) which provide low-latency services to mo
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XML is being incorporated into the foundation of E-business data applications. This paper addresses the problem of the freeform information that stored in any organization and how XML with using this new approach will make the operation of the search very efficient and time consuming. This paper introduces new solution and methodology that has been developed to capture and manage such unstructured freeform information (multi information) depending on the use of XML schema technologies, neural network idea and object oriented relational database, in order to provide a practical solution for efficiently management multi freeform information system.
In this paper the use of a circular array antenna with adaptive system in conjunction with modified Linearly Constrained Minimum Variance Beam forming (LCMVB) algorithm is proposed to meet the requirement of Angle of Arrival (AOA) estimation in 2-D as well as the Signal to Noise Ratio (SNR) of estimated sources (Three Dimensional 3-D estimation), rather than interference cancelation as it is used for. The proposed system was simulated, tested and compared with the modified Multiple Signal Classification (MUSIC) technique for 2-D estimation. The results show the system has exhibited astonishing results for simultaneously estimating 3-D parameters with accuracy approximately equivalent to the MUSIC technique (for estimating elevation and a
... Show MoreCommunity detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective
... Show MoreSystemic lupus erythematosus (SLE) is a multisystem autoimmune disease that affects 43.7 per 100,000 people worldwide, most commonly presenting in childbearing years. SLE pregnancies are complicated by cardiovascular events in up to 7.8% of cases, which translates to a 3.2- to 31.5-fold increase in severe maternal morbidity and a seven-fold increase in maternal mortality, compared to the general obstetric population. The highest risk is reported in cases with concurrent lupus nephritis or antiphospholipid syndrome [1]. These complications are not surprisingly seen; they are the end result of endothelial dysfunction, immune aberration, and placental dysfunction that precedes clinical decompensation by weeks [2,3].The current approach in mana
... Show MoreEarly diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings
... Show MoreThe purpose of this study is aimed to lay down an arranged platform suited to Iraqi constructional associations which in charge to carry out multi constructional projects, as it fulfilled management requirements and supervising, so that low - cost projects will be controlled in due term and quality. Based on primary info and observed data collected, the study thesis has been formulated in this way: Iraqi constructional sector bodies which are in charge to implement simultaneously multi constructional projects in need to reformulate its organized structure so that it will be more fitted to management and control of these projects. This thesis includes a
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Future wireless communication systems must be able to accommodate a large number of users and simultaneously to provide the high data rates at the required quality of service. In this paper a method is proposed to perform the N-Discrete Hartley Transform (N-DHT) mapper, which are equivalent to 4-Quadrature Amplitude Modulation (QAM), 16-QAM, 64-QAM, 256-QAM, … etc. in spectral efficiency. The N-DHT mapper is chosen in the Multi Carrier Code Division Multiple Access (MC-CDMA) structure to serve as a data mapper instead of the conventional data mapping techniques like QPSK and QAM schemes. The proposed system is simulated using MATLAB and compared with conventional MC-CDMA for Additive White Gaussian Noise, flat, and multi-path selective fa
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