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- 저자 : Syed Asif Raza Shah (검색결과 2 건)
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Daim Asif Raja,Syed Ghulam Musharraf,Muhammad Raza Shah,Abdul Jabbar,Muhammad Iqbal Bhanger,Muhammad Imran Malik 한국공업화학회 2020 Journal of Industrial and Engineering Chemistry Vol.87 No.-
Polymer capped metal nanoparticles have been used for numerous biomedical and analyticalapplications. In present study, highly stable gold nanoparticles (AuNPs) capped with polypropyleneglycol (PPG) were synthesized using chemical reduction method. The characterization of PPG-AuNPs wasaccomplished by atomic force microscopy (AFM), zetasizer, fourier transform infrared spectroscopy (FT-IR) and UV–vis spectroscopy. Furthermore, PPG-AuNPs were utilized as colorimetric probe for thirdgeneration cephalosporin antibiotic, ceftriaxone (CEF). PPG-AuNPs permitted efficient, selective,quantitative and rapid recognition in concentration range of 0.1–100 mM in presence of numerousother drugs and salts. PPG-AuNPs have great potential for quantitative recognition of ceftriaxone inbiological and environmental samples. Moreover, the developed sensor has capacity to be applied asquality control of pharmaceutical formulations containing ceftriaxone. The PPG-AuNPs based sensorpermits quantitative and fast recognition of ceftriaxone away from a sophisticated laboratory setup.
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Hussain,신정훈,Syed Asif Raza Shah,조금원 한국멀티미디어학회 2023 멀티미디어학회논문지 Vol.26 No.12
Super-resolution (SR) stands as a prominent challenge in computer vision with diverse applications. Generative adversarial networks (GANs) yield impressive SR outcomes by restoring high-quality images from low-resolution input. However, GAN-based SR (particularly generators) have high memory demands, leading to performance degradation and energy consumption, making them unsuitable for resource-limited devices. Addressing this concern, our paper introduces a novel and efficient SR-GAN (generator) model architecture by strategically leveraging knowledge distillation, which results in reducing storage demands by 58% while enhancing performance. Our approach involves extracting feature maps from a resourceintensive model to design a lightweight model with minimal computational and memory requirements. Experiments across several benchmarks demonstrate that the proposed compressed model outperforms existing knowledge distillation-based techniques, particularly in regard to SSIM, PSNR, and overall image quality in x4 super-resolution tasks. In the future, this compressed model will be implemented and benchmarked with existing models in resource-limited devices such as tablet and wearing devices.
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