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Masoud Alajmi
Taif University
5Publications
Publications 5
Newest
#1Masoud Alajmi (WMU: Western Michigan University)
#2Khalfalla Awedat (WMU: Western Michigan University)H-Index: 1
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Solar energy has proven to be an undisputed frontrunner among renewable energy sources: it is clean, environmentally responsible, and cost-effective. Current methods for fault detection and localization in PV arrays, however, are largely inefficient and labor intensive. In this paper we have developed an efficient technique using IR Thermal Energy Analysis to detect and localize hot-spot faults. Infrared rays are used to produce sequential thermal images of the PV array. The proposed MATLAB algo...
#1Masoud Alajmi (Taif University)
#2Khalfalla Awedat (PLU: Pacific Lutheran University)
Last.Osama S. Faragallah (Menoufia University)H-Index: 12
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In the sparse representation-based classification (SRC), the object recognition procedure depends on local sparsity identification from sparse coding coefficients, where many existing SRC methods have focused on the local sparsity and the samples correlation to improve the classifier performance. However, the coefficients often do not accurately represent the local sparsity due to several factors that affect the data acquisition process such as noise, blurring, and downsampling. Therefore, this ...
#1Khalfalla Awedat (PLU: Pacific Lutheran University)
#2Almabrok Essa (UD: University of Dayton)H-Index: 3
Last.Masoud Alajmi (Taif University)
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In this paper, a new sparse representation based classification technique has been proposed, named modular sparse representation based classification (MSRC). The MSRC is using a block-wise strategy (modular approach) for the sparse representation based classification. The MSRC is based on dividing the face images into non overlapping blocks, which leads to possibility of use overcomplete data dictionary without applying any of data dimensionality reduction methods. However, there will be a big c...
#1Masoud Alajmi (Taif University)
#2Osama Aljasem (Taif University)
Last.Ikhlas Abdel-Qader (WMU: Western Michigan University)H-Index: 11
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Most solar power stations contain hundreds, even thousands, of photovoltaic (PV) modules. Monitoring a solar power station and diagnosing faults in real time are a primary challenge in maintaining the normal operation. A traditional fault detection process is cumbersome and time consuming. In this paper, we are applying a hybrid method for fault detection and localization in serial-parallel configuration using a network of current-voltage sensors-based framework to detect and localize open-circu...
#1Khalfalla Awedat (PLU: Pacific Lutheran University)
#2Masoud Alajmi (Taif University)
Last.James R. Springstead (WMU: Western Michigan University)H-Index: 3
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Mass spectrometry (MS) is a technique that is applied in chemical and biomedical applications for molecular analysis. MS data has extremely high dimensionality that can be represented as a three dimension(3D) dataset. In this paper, we exploit 3D data structure and propose an effective model of compressive sensing (CS) for dimensionality reduction of MS data. A large set of MS data can be reduced significantly with high quality of data recovery. Our recovery approach is based on clustering the M...
Jul 1, 2016 in NAECON (National Aerospace and Electronics Conference)
#1Khalfalla AwedatH-Index: 1
#2Masoud Alajmi (Taif University)
Last.James R. Springstead (WMU: Western Michigan University)H-Index: 3
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In this paper, we propose an efficient technique for dimensionality reduction of Mass Spectrometry (MS) data by employing Compressive Sensing (CS). Not only can CS significantly reduce MS data dimensionality, but it also will allow for full reconstruction of original data. The framework developed in this work is based on forming Sparse Difference (SD) to sparsify MS signals and implementing the Block Sparse Bayesian Learning (BSBL) to reconstruct MS data from its low dimension feature space. Our...
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