Portable Image Based Moon Date Detection and Declaration: System and Algorithm Code Sign

Moshayedi, Ata Jahangir; Chen, Zu-yan; Liao, Liefa; Li, Shuai

doi:10.1109/civemsa45640.2019.9071604

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…After that, SAZ in the concentric circle's step calculates the percentage of every part and compares its value with the origin data. Finally, given the differences, SAZ will give the phase declaration [11]. This algorithm needs seven libraries named NumPy, pillow, scipy, matplotlib scikit, learn, and scikit-image to implement on python.…”

Section: Methodsmentioning

confidence: 99%

“…The designed system as it is shown (Figure 3(a) and Figure 3(b)) includes the Raspberry Pi board and camera, a 7-inch liquid crystal display (LCD) and graphical user interface (GUI) section (Figure 3(c)) to interact with the user. The designed system is elaborated in our previous research paper [11].…”

Section: Hardware Designed and System Componentmentioning

confidence: 99%

“…New developments in the embedded system allow the researcher to test a new system with features like small size, fast speed and low cost (such as Raspberry Pi) [10].Our previous research paper investigated these problems, including portable systems with the Sunfa Ata Zuyan (SAZ) algorithm. The system was designed to capture and analyze the real moon pictures and estimate the moon date [11].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 3. The designed hardware part (a) the system block, (b) design hardware, and (c) designed GUI[11] …”

mentioning

confidence: 99%

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Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

et al. 2022

Self Cite

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Section: Methodsmentioning

confidence: 99%

Section: Hardware Designed and System Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Figure 3. The designed hardware part (a) the system block, (b) design hardware, and (c) designed GUI[11] …”

mentioning

confidence: 99%

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Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

et al. 2022

Self Cite

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“…The authors reached an accuracy ratio of 64.61%. In 2019, Moshayedi et al [13] studied the knowledge and identification of the moon's shapes, the design of the converted Raspberry Pi base system was considered. They designed a system with the ability to capture images, perform image preprocessing with the help of a converted graphical user interface (GUI) written in Python.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Ogla

Saeid

Shaker

2022

IJECE

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<p><span>The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.</span></p>

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Automated brain tumour segmentation from multi‐modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method

Gtifa

Hamdaoui

Sakly

2022

Robotics Computer Surgery

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Background: Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload and improve the accuracy of segmentation. Methods:This paper proposes a level set method constrained by an intuitive artificial intelligence-based approach to perform brain tumour segmentation. By studying 3D brain tumour images, a new segmentation technique based on the Modified Particle Swarm Optimisation (MPSO), Darwin Particle Swarm Optimisation (DPSO), and Fractional Order Darwinian Particle Swarm Optimisation (FOD-PSO) algorithms were developed. Results: The introduced technique was verified according to the MICCAI RASTS 2013 database for high-grade glioma patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient to prove the performance and robustness of our 3D segmentation technique. Conclusion:The result is that the MPSO algorithm consistently outperforms the DPSO and FO DPSO.

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Portable Image Based Moon Date Detection and Declaration: System and Algorithm Code Sign

Cited by 7 publications

References 3 publications

Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

Automated brain tumour segmentation from multi‐modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method

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