scholarly journals Biomimicry and the Built Environment, Learning from Nature’s Solutions

2021 ◽  
Vol 11 (16) ◽  
pp. 7514
Author(s):  
Elmira Jamei ◽  
Zora Vrcelj
Keyword(s):  
Structural Design ◽  
Energy Efficient ◽  
Structural Engineering ◽  
Review Paper ◽  
Built Environments ◽  
Efficient Design ◽  
Material Technology ◽  
Comprehensive Overview ◽  
Comprehensive Review ◽  
Advanced Material

The growing interest in biomimicry in built environments highlights the awareness raised among designers on the potentials nature offers to human and system function improvements. Biomimicry has been widely utilized in advanced material technology. However, its potential in sustainable architecture and construction has yet to be discussed in depth. Thus, this study offers a comprehensive review of the use of biomimicry in architecture and structural engineering. It also reviews the methods in which biomimicry assists in achieving efficient, sustainable built environments. The first part of this review paper introduces the concept of biomimicry historically and practically, discusses the use of biomimicry in design and architecture, provides a comprehensive overview of the potential and benefits of biomimicry in architecture, and explores how biomimicry can be utilized in building envelops. Then, in the second part, the integration of biomimicry in structural engineering and construction is thoroughly explained through several case studies. Finally, biomimicry in architectural and structural design of built environments in creating climate-sensitive and energy-efficient design is explained.

Error-aware Design Procedure to Implement Energy-efficient Approximate Squaring Hardware

2020 ◽  
Vol 10 (4) ◽  
pp. 471-477
Author(s):  
Merin Loukrakpam ◽  
Ch. Lison Singh ◽  
Madhuchhanda Choudhury
Keyword(s):  
Energy Saving ◽  
Relative Error ◽  
Energy Efficient ◽  
Piecewise Linear ◽  
Arithmetic Operation ◽  
Design Procedure ◽  
Digital Signal ◽  
Maximum Relative Error ◽  
Square Function ◽  
Efficient Design

Background:: In recent years, there has been a high demand for executing digital signal processing and machine learning applications on energy-constrained devices. Squaring is a vital arithmetic operation used in such applications. Hence, improving the energy efficiency of squaring is crucial. Objective:: In this paper, a novel approximation method based on piecewise linear segmentation of the square function is proposed. Methods: Two-segment, four-segment and eight-segment accurate and energy-efficient 32-bit approximate designs for squaring were implemented using this method. The proposed 2-segment approximate squaring hardware showed 12.5% maximum relative error and delivered up to 55.6% energy saving when compared with state-of-the-art approximate multipliers used for squaring. Results: The proposed 4-segment hardware achieved a maximum relative error of 3.13% with up to 46.5% energy saving. Conclusion:: The proposed 8-segment design emerged as the most accurate squaring hardware with a maximum relative error of 0.78%. The comparison also revealed that the 8-segment design is the most efficient design in terms of error-area-delay-power product.

Energy efficient design of building: A review

2012 ◽  
Vol 16 (6) ◽  
pp. 3559-3573 ◽  
Author(s):  
R. Pacheco ◽  
J. Ordóñez ◽  
G. Martínez
Keyword(s):  
Energy Efficient ◽  
Efficient Design ◽  
Energy Efficient Design

Energy-efficient design of wavelength-routing networks

2010 ◽  
Author(s):  
A. Bianco ◽  
E. Bonetto ◽  
D. Cuda ◽  
G. Gavilanes Castillo ◽  
M. Mellia ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Efficient Design ◽  
Wavelength Routing ◽  
Energy Efficient Design

scholarly journals The Understanding of Deep Learning: A Comprehensive Review

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ranjan Kumar Mishra ◽  
G. Y. Sandesh Reddy ◽  
Himanshu Pathak
Keyword(s):  
Deep Learning ◽  
Deep Neural Network ◽  
Computational Models ◽  
Review Paper ◽  
State Of The Art ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Levels Of Abstraction ◽  
Comprehensive Review ◽  
Computer Based

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.

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