automation – CRI Report

Event-Based Vision Systems

admin — Sat, 12 Sep 2020 05:18:59 +0000

Latest AI-driven advancements in computer vision focus on emulating the characteristics of the human eye in a vision sensor system. Also known as a neuromorphic or event-based vision system, or dynamic vision sensor (DVS) camera, the system can potentially transform the computer vision landscape by ensuring reduced latency and lower power consumption for upcoming solutions. Its potential application areas include autonomous vehicles (for lower latency, HDR object detection, and low memory storage needs), robotics, IoT (for low power, always on devices), augmented reality/virtual reality (AR/VR) (low power and low-latency tracking), and other industrial automation use cases.
This report focuses on assessing the challenges involved in the adoption of event-based vision systems, and the solutions and approaches that the active participants are developing for introducing innovative products. The report combines a comprehensive analysis of patent filings, companies active in the space, and R&D activities from universities and research labs across the world, delivering key insights into the maturity and evolution of the technology.
Patent Trend Analysis
Patent filings over the last decade (2010-2019) were analyzed to evaluate the level of participation of various entities in the R&D space. This section details the assignee landscape and key patents in the domain. The different patent filings have been studied to understand the key challenges addressed by the patent publications. Additionally, patent filings related to event-based vision technologies with a focus on automotive applications are described in-depth to highlight the different deployment scenarios spanning the sector.
Competitive Intelligence
The section provides a detailed description of established companies, startups, and research institutes working on event-based cameras. Different parameters, including company overview, technology stack, partnerships, key personnel, future roadmap, and limitations have been considered for a comprehensive competitive profiling.
A key highlight to emerge from this analysis is that several European startups are directly competing against Samsung in the event-based vision technology domain.
Further, a benchmarking matrix of the commercialized and in-pipeline products has also been included for an in-depth analysis.
Companies mentioned in the report
1. Prophesee
2. iniVation
3. Insightness
4. Qelzal
5. MindTrace
6. CelePixel
7. Sunia
8. Australian Institute of Technology
9. Samsung
10. Sony
Key Insights
 Event-based vision systems overcome the issue related to redundant information in the traditional frame-based vision systems.
 Event-based technology is at the early stages of development and significant research and investments are increasingly focusing on accelerating the development of such systems.
 Event-based vision techniques are being explored in the automotive sector for both in-car applications and for scenarios outside the vehicle.
 Some early adopters of the technology are focusing on the DVS fabrication processes and on pixel size reduction.
 Samsung is amongst the earliest adopters of the DVS technology.
 Research laboratories are focusing on emulating the various parameters of DVS to address challenges such as low dynamic range, pixel size, motion blur, and high latency.
 Event-based vision systems are finding application in self-driving cars, drones, IoT, robotics, wearable devices, and surveillance.
Key questions addressed in the report:
• What is the difference between frame-based vision and event-based vision?
• How do event-based vision techniques overcome the limitations of a traditional frame-based vision system?
• What are the key challenges faced by the technology, and which are the entities addressing them?
• What is the patent filing trend for the technology between the years 2010-2019?
• How is event-based vision technology transforming the automotive sector?
• What is the competitive scenario for event-based vision technology?
• What are the projects and research activities related to the technology?
• Which universities and research institutes, active in the domain, should you watch?
• What are the different collaborations and investment opportunities for new companies seeking to explore event-based vision technologies?

The post Event-Based Vision Systems first appeared on CRI Report.

Global Logistics Automation Market (2018-2023) Share, Scope, Revenue, Trends, Drivers, Challenges, Segmentation based on Component (Hardware, Software, Services) End Use Industry (E-commerce and retail, Automotive, Aerospace, Food and beverage, others) and Geography (North America, Asia Pacific, The Middle East and Africa, Europe and Latin America)

admin — Sat, 12 Sep 2020 05:18:15 +0000

In recent years, logistics automation has emerged as a necessity for companies. Changes in customers’ preferences are resulting in the expansion of product categories, which makes handling them difficult, given the constraints of existing supply chain practices. Customers’ expectations have also increased, with concepts like ‘last-mile delivery’ and ‘delivery on-demand’ becoming common. To improve operational efficiency and remain competitive, companies are opting for logistics automation. The global logistics automation market is expected to expand at a compound annual growth rate (CAGR) of 12.6% and will be worth USD 100.1 Bn by 2023.

Segmentation based on component:
The hardware segment is expected to hold a market share of more than 50% during the forecast period and will grow at a CAGR of 10.7%. Even though most companies prefer hardware products like automated conveyor systems, lift trucks, automated racks, and robotic picking for implementing logistics automation, the need for capital investments is a major deterrent. As a result, some of them opt for software solutions instead, since they are easy to integrate and are cost-effective. This segment is expected to expand at a significant CAGR of 16.0%, the highest under this categorization. Moreover, as companies try to reduce their capital investments, tasks are likely to be handed over to third-party logistics automation service providers.

End use industry segment insights:
The exponential expansion of the e-commerce industry, expectations regarding swifter deliveries, and intense competition among market players are some of the major factors contributing to the growth of the logistics automation market. Thus, the e-commerce and retail segment is expected to be the highest growing end use industry in terms of logistics automation, expanding at a CAGR of 16.9% during the analysis period. By 2023, this segment is expected to hold a market share of more than 40%.

The online product delivery ecosystem and the growing preference for air transport are expected to result in a significant increase in the volume of air cargo in the coming years. To cope up, airports around the world are adopting logistics automation to increase their capacities (despite the limited availability of land), and to ensure error-free and safe handling of large volumes of freight.

Logistics automation also finds extensive applications in the food and beverage industry. The technology is well-suited for accurate product measurements, flawless packaging, and for performing tedious, repetitive tasks in tough environments like freezer warehouses.

Regional insights:
Europe and North America are mature markets, which dominated the global logistics automation sector with market shares of 33.6% and 29.2% respectively, in 2018. Most of the key players in the market are based in Europe. The logistics sector contributes significantly to the gross domestic product (GDP) of countries in Europe, make it a viable avenue for investments in automation. The logistics automation market in the Asia-Pacific and the Middle East and Africa regions are expected to exhibit higher growth in comparison to the other regions, owing to increasing investments in this sector and the presence of some significant players.

Companies covered:
– ABB
– Toyota Industrial Equipment
– KION
– Jungheinrich
– KUKA
– Daifuku
– Murata Machinery
– Beumer Group
– KNAPP
– System Logistics

The post Global Logistics Automation Market (2018-2023) Share, Scope, Revenue, Trends, Drivers, Challenges, Segmentation based on Component (Hardware, Software, Services) End Use Industry (E-commerce and retail, Automotive, Aerospace, Food and beverage, others) and Geography (North America, Asia Pacific, The Middle East and Africa, Europe and Latin America) first appeared on CRI Report.

Global Proximity Sensors Market (2018-2023) Share, Scope, Growth Forecast, Trends, Drivers, Challenges, Segmentation based on Technology (Photoelectric, Capacitive, Inductive, Magnetic, Ultrasonic) End-Use Industry (Consumer Electronics, Aerospace and Defence, Manufacturing, Automotive, Food and Beverage, Metals and Mining, Others) and Geography (North America, Asia Pacific, The Middle East and Africa, Europe and Latin America)

admin — Sat, 12 Sep 2020 05:18:08 +0000

The implementation of proximity sensors is transforming the business prospects of several industries by automating production operations, and improving safety and security solutions. With the emergence of Industrial Internet of Things (IIoT) in the manufacturing sector, there has been an increase in demand for components that are capable of acquiring and transmitting information regarding production processes. This is one of the key factors driving the adoption of proximity sensors. The proximity sensors market is projected to expand at a compound annual growth rate (CAGR) of 7.5% during the forecasted period, and will reach a value of USD 3.3 Bn by 2023.
Technology segment insights:
The photoelectric sensors segment held the largest market share in 2018, and is predicted to expand at the highest CAGR, during the forecasted period. This growth can be attributed to the introduction of 3D LiDAR technology that consists of Time-of-Flight (ToF) sensors. Owing to its ranging and 3D-mapping capabilities, 3D LiDAR technology has several applications in fields like robotics, medical, automotive, and spacecraft systems.

End use industry segment insights:
Industries like consumer electronics, aerospace and defense, manufacturing, automotive, food and beverage, metals and mining, and ‘others’, are gradually realizing the potential of proximity sensors in their business processes. In 2018, the consumer electronics segment held the largest share (24.3%) of the market, owing to the contribution of proximity sensors in gesture recognition features of smartphones, smart lighting systems, and auto-regulation of temperature in smart homes. The manufacturing segment is predicted to expand at the highest CAGR (10.2%) during the forecasted period (2018-2023). This is primarily because of the high adoption of photoelectric, inductive, and capacitive sensors in the sector to automate monitoring and management of production processes.

Regional insights:
Europe dominated the global proximity sensors market with a share of 34.8% in 2018 since most of the key players are based in this region. The proximity sensors market in the Asia-Pacific region is estimated to exhibit the highest growth rate, owing to the higher demand for better infrastructure, smart home solutions, and improved transportation systems to meet the needs of a growing population.

Companies covered:
– ST Microelectronics NV

– Semtech Corporation

– Rockwell Automation Inc.

– Panasonic Corporation

– IFM Electronic GmbH

– Sick AG

– Pepperl + Fuchs GmbH

– Schneider Electric SE

– Broadcom Inc.

The post Global Proximity Sensors Market (2018-2023) Share, Scope, Growth Forecast, Trends, Drivers, Challenges, Segmentation based on Technology (Photoelectric, Capacitive, Inductive, Magnetic, Ultrasonic) End-Use Industry (Consumer Electronics, Aerospace and Defence, Manufacturing, Automotive, Food and Beverage, Metals and Mining, Others) and Geography (North America, Asia Pacific, The Middle East and Africa, Europe and Latin America) first appeared on CRI Report.

Reinforcement Learning – Startup Ecosystem Analysis

admin — Sat, 12 Sep 2020 05:18:08 +0000

Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time.

With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems. The technology appears set to drive the adoption of AGI technologies, with companies futureproofing their AGI roadmaps by leveraging reinforcement learning techniques.

This report provides an analysis of the startups focused on reinforcement learning techniques across industries.
Startup Ecosystem
We describe the importance of agent-based learning methods and the core concepts related to reinforcement learning. The section includes a comparison of machine learning techniques, and an overview of topics like imitation learning, AGI, and model-based and model-free reinforcement learning algorithms. Further, we outline how reinforcement learning techniques can help address the current challenges of various industries in developing next-generation solutions.

The report includes an analysis of startups leveraging reinforcement learning algorithms in key sectors like automotive, retail, industrial, financial services, robotics, healthcare, IoT, food industry, and several others. We cover 39 startups, out of which some prominent startups like Osaro, Kindred, Micropsi Industries, Wayve, Cerebri AI, and OpenAI have been reported in detail.

Analysis of these startups has been undertaken basis their technologies, offerings, patenting activities, and future outlooks. Additionally, an overview of other startups including Latent Logic, NeuDax, Nnaisense PerimeterX, Deeplite, and Context Scout, which are leveraging reinforcement learning algorithms, has been covered in the ambit of the report.

Startups covered in the report
1. Osaro
2. OpenAI
3. Acutronic Robotics
4. Wayve
5. Kindred
6. Prowler.io
7. Cerebri AI
8. Micropsi Industries
9. CogitAI
10. InstaDeep
11. Latent Logic
12. NeuDax
13. Ascent Robotics
14. Context Scout
15. Wasteless
16. Insilico Medicine
17. ProteinQure
18. Evo.Do
19. Remi
20. Optimal
21. AiGent-Tech
22. Learnable AI
23. HiHedge
24. Covariant.AI
25. AIdentify
26. Nnaisense
27. PerimeterX
28. Incelligent
29. Cogent Labs
30. Pricemoov
31. Dataone
32. Intelligent Layer
33. Kinta AI
34. Omina Technologies
35. Deeplite
36. Free Machines
37. Dorabot
38. Applied Brain Research
39. Neurocat

Key insights:
– Reinforcement learning is a prime technology for the of future self-learning, self-optimizing and other self-driving abilities needed in autonomous applications across industries.
– Automotive, retail, ecommerce, and robotics is crowded with startups developing reinforcement learning techniques.
– Reinforcement learning can address the requirements related to dynamic decision-making in autonomous vehicles targeting level 5 autonomy.
– Other sectors exploring reinforcement learning are healthcare, financial services, food industry, manufacturing, education and telecom.
– Startups that are offering reinforcement learning techniques in robotic solutions are focusing on robots as a service business model.

Key questions addressed:
– What are the advantages offered by reinforcement learning algorithms in comparison with the other machine learning models?
– What are the basic concepts in reinforcement learning?
– How can reinforcement learning address the technical requirements of different sectors and emerging applications?
– What is nature and size of the startup ecosystem for reinforcement learning and how is it changing the landscape of traditional artificial intelligence?
– What are the target industries and application areas for the new companies?
– What are the technologies, future roadmap, partnership activities, limitations, and offerings of the reinforcement learning- focused startups across industries?
– What are the potential collaboration opportunities for different industry segments to leverage reinforcement learning solutions for their businesses?

The post Reinforcement Learning – Startup Ecosystem Analysis first appeared on CRI Report.