Simulation study on CCD tomography system for ruby stone optical properties

Ninety percent of the ruby stones available worldwide come from Myanmar. Malaysia is known to be one of the countries that have been importing ruby stones for precious stone industries, manufacturing industries, medical and dentistry applications. There are several gemology tools which are used to investigate the grading of ruby stones such as loop, microscope, and dichroscope. Nevertheless, these tools are highly dependable on human visual assessment and require years of experience that may lead to error since ruby stone quality is evaluated based on its clarity and transparency. Hence, this paper addresses a simulation study on the optical properties of ruby stones via Charge-Coupled Device (CCD) Tomography approach. This paper indicates the capability of CCD and tomography system to analyze the ruby stone optical properties through image reconstruction based on the previous research. Linear Back Projection (LBP) algorithm will be used to construct two-dimensional image reconstruction of varieties ruby stones. From these image reconstructions, the transparency and blemishes of ruby stones can be analyzed

Analysis on clarity of rubies gemstones using charge-coupled device (CCD)

Ruby is one of the most precious gemstones on Earth that is always high in demand especially in the jewelry industries. Due to its high value and very expensive, a lot of imitation of ruby has been made. This results in the rising of more complicated issues as gemologists need to perform the grading valuation very carefully and precisely. The current and common grading techniques mostly depend on human vision, which eventually leads to error. This paper aims to analyze the clarity of rubies gemstones using Charge-Coupled Device (CCD). The CCD detects the light intensity and then convert the light intensity value into the voltage value. The CCD sensor is very special in its architecture design, consisting of more than 1000 very small pixels that are sensitive to light sources. Based on the previous research, CCD has high sensitivity to laser light source with wavelength range within 430 nm to 650 nm. This research is going to prove that CCD is able to detect the clarity of various grading of the pink to blood-red ruby stones

Strategi pembelajaran kolokasi bahasa Arab dalam kalangan pelajar universiti awam Malaysia

Strategi pembelajaran kolokasi merupakan strategi yang dipandang sepi dalam kurikulum bahasa Arab, namun para sarjana mengakui bahawa ia adalah strategi dominan yang digunakan dalam kalangan pelajar bahasa asing yang mempunyai keupayaan di tahap sederhana. Kajian ini meninjau tahap penggunaan strategi pembelajaran kolokasi bahasa Arab dalam kalangan pelajar bahasa Arab di universiti awam Malaysia. Untuk tujuan itu, satu set soal selidik jenis skala Likert telah diedarkan kepada pelajar bahasa Arab yang sedang mengikuti pengajian di universiti awam dan sudah berada pada tahun akhir pengajian. Kajian ini menggunakan pensampelan rawak mudah. Seramai 344 orang pelajar daripada lapan buah universiti awam menyertai kajian ini. Dapatan kajian menunjukkan bahawa strategi pembelajaran kolokasi dalam bahasa Arab adalah dominan walaupun berada pada skala penggunaan yang sederhana. Kajian ini mencadangkan strategi pembelajaran kolokasi bahasa Arab yang baru berdasarkan substrategi pengecaman, masteri teknik (kefahaman, pengulangan, penonjolan, kamus, sosial dan imejan) serta aplikasi. Selain dari itu, dapatan kajian ini juga menunjukkan bahawa strategi pembelajaran kolokasi perlu dimanfaatkan oleh pelajar terutamanya dalam memainkan peranan autonomi bagi meningkatkan kompetensi dalam penggunaan bahasa Arab terutamanya aspek kolokasi

Image detection and classification of oil palm fruit bunches

Many computer vision approaches, such as deep learning and machine learning, can be employed in agriculture in this era of artificial intelligence (AI). These computer vision techniques are frequently utilized in product categorization, identification, and estimation. Implementing computer vision in agriculture will boost agricultural output and quality by assisting farmers in monitoring agricultural activities. Currently, the oil palm harvester judges oil palm maturity manually using natural signs like oil palm colour appearance and the number of oil palm loose fruit drops under the tree. This paper focuses on developing automated detection systems using a deep learning model to detect and classify oil palm fruit bunch based on their ripeness level. The ripeness of oil palm can be classified into four maturity levels: unripe, under ripe, ripe, and over ripe. There are two approaches in this paper namely detection of oil palm fruit bunches and classification of oil palm fruit bunches. This paper employes several types of YOLO algorithms such as YOLOv3, YOLOv3-Tiny, YOLOv4, and YOLOv4-Tiny to compare the performance and accuracy of different versions of YOLO. It is shown that YOLOv4 has a higher accuracy of 98.70% in detecting and classifying oil fruit bunches based on their ripeness level compared to YOLOv3, YOLOv3-Tiny, and YOLOv4-Tiny

Analysis on clarity of rubies gemstones using charge-coupled device (CCD)

Ruby is one of the most precious gemstones on Earth that is always high in demand especially in the jewelry industries. Due to its high value and very expensive, a lot of imitation of ruby has been made. This results in the rising of more complicated issues as gemologists need to perform the grading valuation very carefully and precisely. The current and common grading techniques mostly depend on human vision, which eventually leads to error. This paper aims to analyze the clarity of rubies gemstonesusing Charge-Coupled Device (CCD). The CCD detects the light intensity and then convert the light intensity value into the voltage value. The CCD sensor is very special in its architecture design, consisting of morethan 1000 very small pixels that are sensitiveto light sources.Based on the previous research, CCD has high sensitivity to laser light source with wavelength range within 430 nm to 650 nm.This research is going to prove that CCD is able to detect the clarity of various grading of the pink to blood-red ruby stones