Nanotechnology Innovations in Smart Textiles, Biofuels, Crop Cultivation and Medicine

  • ID: 4437219
  • Report
  • 15 pages
  • Frost & Sullivan
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This issue of Nanotech TOE covers innovations in healthcare, processing, and optoelectronics, The innovations profiled include a novel polymeric nanoparticles to track damaged tissues, a cost-efficient process to mix oil and water, a drug delivery system, a novel process to impact production of 2D materials, and a color sensing device. The Nanotech TechVision Opportunity Engine (TOE) provides intelligence on technologies, products, processes, applications, and strategic insights on nanotechnology-related innovations and their impact across various industries. Technology focus areas include nanomaterials, nanocoatings, nanohealthcare, nanomedicine, and nanomanufacturing. The Chemicals and Advanced Materials cluster tracks research and innovation trends and developments across specialty chemicals, plastics, polymers, chemicals, bio-chemicals, metals, coatings, thinfilms, surface treatments, composites, alloys, oil and gas, fuel additives, fibers, and several other related technologies and its impact and application across industries.

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1. Executive Summary

  • Key Findings
  • Top Trends Driving the Development of AI for AD
  • Levels of Automation Defined With Regard to AI
  • Expanding Universe of AI in AD—Vital Pillars
  • Value Chain Development of AI in Universe of AD
  • Noteworthy Companies With AI Capabilities—By Region
  • Major Tech Companies’ Approach—Overview
  • Adjoining Revenue Opportunities for Artificial Intelligence in AD
  • Major Challenges in Implementation of AI in AD
  • Key Trends

2. Research Scope and Segmentation

  • Research Scope
  • Key Questions This Study will Answer

3. Automated Driving Artificial Intelligence versus Traditional Approach

  • Traditional Approach Versus Deep Learning Approach
  • AI—Key Differentiators
  • Dependence of AI Development on Software
  • Progression of AI in Autonomous Vehicles
  • Disruption in the Automotive Industry with Developing AI
  • Role of Data Flow in AI in AD Cars

4. Deep Learning in AI

  • DNN to Drive Self-learning AI
  • Deep Neural Network—Training Cycle
  • Challenges for Deep Learning Adoption for AD
  • Machine Learning Approach—Case Study: Oxbotica
  • Deep Learning Approach—Case Study 1: Drive.ai
  • CNN—Case Study: AIMotive

5. Innovation Through Partnerships

  • NVIDIA—A Complete End-to-end AI Solution: Hardware
  • NVIDIA—A Complete End-to-end AI solution: DL Software
  • NVIDIA’S Activity—Highlighted Partnerships
  • Companies Ahead in the Business—Overview

6. Major OEM Activities

  • Major OEMs and AI—How They Rate Against Each Other?

7. Growth Opportunities and Companies to Action

  • Growth Opportunity—Investments and Partnerships from OEMs/TSPs
  • Strategic Imperatives for Success and Growth

8. Conclusions and Future Outlook

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