الصفحة 1
الصفحة 1
Nanotechnology for Smart Concrete
Nanomaterials can markedly improve the mechanical properties of concrete, as well as reduce the porosity and enhance the durability of concrete. The application of nanotechnology in concrete is still in its infancy. However, an ever-growing demand for ultra-high-performance concrete and recurring environmental pollution caused by ordinary Portland cement has encouraged engineers to exploit nanotechnology in the construction industry. Nanotechnology for Smart Concrete discusses the advantages and applications of nanomaterials in the concrete industry, including high-strength performance, microstructural improvement, self-healing, energy storage, and coatings. The book: Analyses the linkage of concrete materials with nanomaterials and nanostructures / Discusses the applications of nanomaterials in the concrete industry, including energy storage in green buildings, anti-corrosive coatings, and inhibiting pathogens and viruses / Covers self-healing concrete / Explores safety considerations, sustainability, and environmental impact of nanoconcrete / Includes an appendix of solved questions
Deep learning and computer vision in remote sensing-II
Computer vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing.
Deep learning and computer vision in remote sensing-I
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
Clock Generators for SOC Processors : Circuits and Architectures
On the architectural level, the discussion includes PLL analysis using continuous-time as well as discre- time models, linear and nonlinear effects of PLL performance, and detailed analysis of locking behavior.
Artificial intelligence based cancer nanomedicine : Diagnostics, therapeutics and bioethics
Nanomedicine is evolving with novel drug formulations devised for multifunctional approaches towards diagnostics ad therapeutics. Nanomedicine-based drug therapy is normally explored at a fixed dose. The drug action is time-dependent, dose-dependent and patient-specific. To overcome challenges of nanomedicine testing, artificial intelligence (AI) serves as a helping tool for optimizing the drug and dose parameters. Real time conversions between these two features enables upgradation of patient data acquisition and improved design of nanomaterials. In this scenario, AI-based pattern analysis and algorithms models can greatly improve accuracy of diagnostics and therapeutics.
