http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Murtaza Hussain Shaikh,Ivevel S. Karlson,김종호 사단법인 미래융합기술연구학회 2018 아시아태평양융합연구교류논문지 Vol.4 No.4
The notion of the Internet of Things takes labelled by a grouping of names, mainly comprising on automaton to automaton (M2M), sensor grids, hyper-pervasive computing and smart ubiquitous enumeration. Singularities of improved Internet of Things in healthcare supervision systems include a patient to be recognized assigning an RFID chip has also expanded the peril to the information and essential materials. This paper plugs over the patient partiality based privacy solidification set-up for the Internet of Things centred on the ubiquitous healthcare management system (HCMS), famously known generally for the United Kingdom and Northern Ireland `s healthcare domain. To pledge a British patient’s confidentiality to his information, especially in this article the study has tried to describe some real settings and practical stints, which if implemented would decline the risk of data mugging in the hospital information system. The research demographics of this study is the public hospital (Queen Mary`s hospital) in London, United Kingdom. For that reason according to the demographic settings, this article familiarizes some advanced methodologies, which are implemented in a diverse automated system, and have been employed into Internet of Things based healthcare supervision system.
Murtaza Hussain Shaikh,Armigon Ravshanovich Akhmedov,Muzaffar Makhmudov 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.2
Recent information and communication technologies have already opened up new prospects for technology groups, especially in a knowledge-based society. A contemporary technological era, which can be stated as the hyper-connective Internet of Things surpassed the traditional service pattern and innovation pattern by conveying personalized, localized, and con-text-aware services close to different actors and users. The conventional boundary of the organization is disbanding as well as traditional innovation and research & development limits. This research article conducts a preliminary study about the hyper-connective Internet of Things technology portent with innovation 3.0 version based on an extended technological organization environment framework (E-TOEF). This article discusses the emergence of innovation 3.0 as a paradigm shift from a manufacturing paradigm to an actor-oriented paradigm. There is a need to shift from a manufacturing mindset to more user ergonomics and be aware of the potential of hyper-connective IoT on the revolution of innovation patterns to be more cooperative, open, and user-centered. Besides, this article would strain some conceptual approaches for the next-generation innovation paradigm known as “hyper-connective IoT” entitled innovation 3.0. This new innovation version goes beyond open innovation and undeniably clearly beyond closed innovation which was an earlier version.
LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution
Muhammad, Wazir,Shaikh, Murtaza Hussain,Shah, Jalal,Shah, Syed Ali Raza,Bhutto, Zuhaibuddin,Lehri, Liaquat Ali,Hussain, Ayaz,Masrour, Salman,Ali, Shamshad,Thaheem, Imdadullah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.12
Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.
LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution
Muhammad, Wazir,Shaikh, Murtaza Hussain,Shah, Jalal,Shah, Syed Ali Raza,Bhutto, Zuhaibuddin,Lehri, Liaquat Ali,Hussain, Ayaz,Masrour, Salman,Ali, Shamshad,Thaheem, Imdadullah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.spc12
Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.