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Edge computing enhanced by AI has countless applications. Let’s take a closer look at its positive impact on data analysis and how businesses can put such data to use.

Cloud computing allows for significant cost cutting in terms of data centers, providing access to resources for storage and calculating capacity that are virtually unlimited and available on-demand. But not all applications are suitable to cloud solutions, which have various potential drawbacks connected to latency and connection reliability. In these cases, a far more preferable solution is edge computing, the system capable of processing information on the device where it’s generated, eliminating the problem of both data transfers and latency. This is even more true when edge computing is combined with AI.

Today’s digital-first businesses and consumers are generating unprecedented volumes of data from devices and systems unimaginable just a decade ago—and this data needs to be captured and leveraged in real time for a growing array of use cases emerging at the edge. Take, for example, medical imaging data requiring realtime processing and display to aid in patient care and machine vision applications whereby high-resolution smart cameras must transfer data immediately to drive manufacturing robotics. Also consider the vast amounts of data generated by public sector systems located in avionics, on submarines, or at field bases that require real-time analysis for decision-making. 

Why should a manufacturing company today, as well as any other organization, embark on a data-driven transformation journey? In 2020, research conducted by Statista measured a 12% increase, compared to 2018, in the adoption of a global data-driven approach for decision-making. Here's what is driving this change.

Edge computing solutions in industrial settings are poised for rapid growth and innovation, driven by the need for real-time insights and localized action. I&O leaders must tread cautiously as vendors begin to collaborate and provide solutions based on temporary and opportunistic alliances.

SECO is named among the Representative Edge Computing Vendors focused on Industrial IoT.

In this paper we propose a retrofitting methodology based on Design Thinking in a steel mill plant, and highlight how the retrofitting activity is important to allow even more companies to migrate to Industry 4.0, reducing the gap between SMEs and Large Industries for participation in the 4th Industrial Revolution.

In the paper we present the design and development of the Machine Learning (ML) modules for two case studies. In both cases we developed a ML model to learn the system’s normal behavior so to identify whichever abnormal condition may arise. Such a framework is usually referred to as Anomaly Detection (also known as Fault Detection or Novelty Detection). Our models succeeded at identifying the injected anomalies. In addition, no anomalies were observed when the model was fed with normal data. The results are discussed considering the trade-off between type of sensors, learning algorithm, training effort, computational demands.

As connectivity and flexibility requirements continue to drive performance demands in embedded edge computers and servers, industries are poised to take advantage. Factories and logistics firms are streamlining their production lines with greater precision using robotics. Hospitals are accelerating medical imaging appliances like ultrasound with AI capabilities.

Public sector and aerospace are also ramping up AI image recognition, event analysis, and security use cases to come up with exciting new efficiency models to improve quality of life in cities.

Performance is driving a new wave of creativity and innovation, and both computing modules and processors need to keep pace through enhanced flexibility.

Maximum degree of automation, more flexibility in production, significantly lower personnel resources: innovative industrial companies hope for a prosperous future through Artificial Intelligence and Machine Learning.
And that is not all: PwC‘s consultants have
found out that such technologies are an
obligatory exercise in staying competitive.

Keeping workers and the environment safe is a key priority when designing automated equipment and processes. Functional Safety (FuSa), a technology to remove unacceptable risks in the presence of a fault, is critical to applications in the industrial automation, agricultural, transportation, and healthcare industries, among others.

As FuSa has gained importance, Smart Mobility ARChitecture (SMARC) modules — low-power, high-performance modules that provide power, DRAM, and compute — have become a standard for many IoT edge applications. Now, SECO has created a new solution that simplifies the adoption of the latest available FuSa technologies
by combining the power of SMARC modules with a feature set focused on FuSa applications.

ATMs are an easy target for fraud attacks, like card skimming/trapping, cash trapping, malware and physical attacks. Attacks based on explosives are a rising problem in Europe and many other parts of the world. A report from the EAST association shows a rise of 80% of such attacks between the first six months of 2015 and 2016. This trend is particularly worrying, not only for the stolen cash, but also for the significant collateral damages to buildings and equipment.