Advanced statistical testing to reduce development costs and improve machine reliability
Testing an automation system, a robot or a complex high-value machine for reliability is a long, expensive, and challenging part of product development. Simon Shakespeare sees a future where product design for reliability is increasingly informed by simulations and statistical approaches to testing, giving engineers confidence in a design before any hardware prototype has been committed to production.
Low-cost satellite IoT: around the corner, but no free lunch for operators
This blog looks at the impact of orbit and operating frequency on the types of IoT service a satellite operator can deliver. Paying attention to IoT network topology and air-interface protocols will enable all operators to make the most of the dish in front of them, says Steve Baker.
What is a good innovation strategy in surgery… and is it robotics?
Robotic surgery is a hot topic: the concept is popular with patients, on the minds of industry leaders, and seen as the future of surgery by many investors. Too often, robotic-assisted surgery is equated with precision, and precision with improved clinical outcomes. Excitement is driving investment and a growing number of robotic-assisted surgery companies. But, in basic terms, what is a good innovation strategy in surgery… and is it robotics?
Beyond intravenous delivery: choosing better administration routes for oncologic drugs
Intravenous delivery remains the dominant paradigm in oncology, but even with precision medicine few drugs pass late stage clinical trials. Karthik Chellappan proposes that early exploration of better administration routes could make all the difference.
Satellite IoT for 5G — What’s the Story?
What does the rise of 5G and new (LEO) space operators mean for the satellite and cellular industries and for next-generation IoT? TTP’s Adrian Hillier charts the way ahead.
Human learning with machine learning: the game of Go and real-life applications
When working with machine learning tools and algorithms, we often assume that only the machine is learning. Not so, argues Mark Hsieh. To achieve maximum value, we need to learn from the results generated by algorithms to better understand the problem we are trying to solve, for example in predicting disease progression in diabetes.
Evolution in user terminal design for broadband from space
New LEO satellite networks promise high-speed broadband access for billions living beyond the reach of fibre and cellular networks. But the cost of User Terminals (UTs) for such satellite connections remains far beyond consumer budgets. Andrew Fell sketches how technical innovation, convergence in standards and network intelligence could pave the road to low-cost UTs for broadband from space.
Smart antennas for the IoT: for the few, not the many?
Smart antennas can radically improve IoT network performance - and they can do this with no changes to the population of wireless IoT devices. Only the IoT gateway terminals – usually relatively few in number – need the upgrade. But ultimately everyone reaps benefits – network operators from improved coverage, data capacity, network resilience and location services, and network users from lower cost and lower power IoT devices.
Could technology help contain the next equine flu epidemic?
Humanity has endured infectious disease epidemics for as long as it’s existed. However, as global connectivity grows so does the rate at which they can spread and the number of people they can affect. The origin of epidemics is the random genetic mutation of pathogens so we have no control over their arrival but technology offers very real control over the way they spread.
How to build working AI models when you are short on training data: AI as part of the business transformation toolkit (Part 3)
The sights of Artificial Intelligence (AI) can be trained on almost any business problem that will benefit from automation. But what if training data for your problem is in short supply? In this blog, Simon Shakespeare shares a few tricks for training AI without reinventing the algorithmic wheel or by creating training data out of thin air.