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Menampilkan 1–6 dari 6 artikel
Unifying Dual-Pyramid Structure and Y–G Channel Synergy for Full-Reference Image Quality Assessment
Advance Sustainable Science, Engineering and Technology
Vol 8
, No 1
(2026)
Standard metrics such as SSIM often overlook complex chromatic distortions, creating a gap between objective scores and human judgment. To address this, we present the Synergistic Structural Similarity Index (SSSI), a metric grounded in a novel dual-pyramid strategy that integrates Gaussian-blurred stability with direct subsampling sharpness. Our method departs from luminance-only analysis by employing an equal, synergistic partnership between the luminance (Y) and Green (G) channels, mirroring...
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Indonesia's Digital Education Revolution: Enhancing Vocational Learning Through Technology-Driven Project-Based Methods
Jurnal Komunikasi Pendidikan
Vol 9
, No 2
(2025)
Indonesia is undergoing a transformative era in vocational education, pushed by the integration of technology-driven, project-based learning (PJBL) methodologies and Learning Management Systems (LMS). This comprehensive study examines the implementation and effectiveness of technology-driven project-based learning at SMK Cut Nya' Dien, utilizing a quasi-experimental design with 35 students divided into experimental and control groups. Using SPSS statistical analysis, the research demonstrates si...
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Performance Comparison of UWB Single Balanced Schottky Diode Mixers for RF Front-End Applications in 3-10 GHz Band
Shairi, Noor Azwan
; Mohammed , Yahya Al-gumaei
; Zakaria, Zahriladha
; Maizatul Alice Meor Said
; Misran, Mohamad Harris
; Abdullah Mohammed, Zaghir Zobilah
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 2
(2025)
This paper compares two single balanced mixer designs for ultra-wideband (UWB) of RF front-end at frequencies ranging from 3 to 10 GHz. The proposed mixer designs use two balun topologies for varying mixer performances. Thus, Design 1 incorporates a Coupled Line Balun and Design 2 incorporates a Branch Line Balun. Both designs make use of Skyworks' SMS7621 Schottky diodes, which have a low junction capacitance, and the Rogers RO4350B substrate, which has a dielectric constant of 3.48. The Coupl...
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1 Sitasi
Advancing Dermatological Image Classification: GLCM-Based Machine Learning Insights
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 1
(2025)
The prospects to improve skin illness via the utilization of artificial intelligence algorithms is what renders this study economically important. Machine learning may assist physicians detect people quicker and more accurately. The effective identification of skin disorders using machine learning could result in the development of large and readily available digital tests. A model was used in the present study to analyze the HAM 10000 data. Two hundred images in total were chosen at random; one...
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1 Sitasi
Advancements and Challenges in Additive Manufacturing: Future Directions and Implications for Sustainable Engineering
Mohammed, Raffi
; Shaik, Abdul Saddique
; Mohammed, Subhani
; Bunga, Kiran Kumar
; Aggala, Chiranjeevi
; Babu, Bairysetti Prasad
; Badruddin, Irfan Anjum
Advance Sustainable Science, Engineering and Technology
Vol 7
, No 1
(2025)
This study explores the recent advancements in additive manufacturing (AM) and its significant effects on various industries such as aerospace, automotive, medical, and casting. The research investigates how AM has the potential to enhance design flexibility, reduce weight, and optimize material performance through developments like adaptive algorithms, topology-based process planning, and multi-objective optimization techniques. These advancements have resulted in near-net-shape casting, improv...
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3 Sitasi
Advances in Deep Learning for Skin Cancer Diagnosis
Advance Sustainable Science, Engineering and Technology
Vol 6
, No 4
(2024)
The most prevalent type of cancer worldwide is known as skin cancer. Early detection is critical because if left undiagnosed in the primary stage, it might be fatal. Although there are differences within the class and high inter-class similarities, it is too difficult to distinguish with the naked eye. Owing to the disease's global prevalence, a number of deep learning based automated systems were created thus far to help doctors identify skin lesions early on. Using pre-trained ImageNet weights...
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1 Sitasi