Category Archive: Technology

Webcast: The Impact of Artificial Intelligence on Modeling, Simulation, & Training

Implications of Physically Based Rendering for Training and Simulation

Simulation experts, visual system engineers and consumers all want the next big thing that will improve experiences and offer enhanced capabilities. NVIDIA Quadro RTX GPUs offering real-time ray tracing that simulates the actual behavior of light and materials represent the best path to meet ever-increasing expectations. This webinar covers how Quadro RTX offers all of the above – and much more – including performance scalability, support for ultra-high resolution displays, High Dynamic Range (HDR) imagery, and the latest advances in Virtual Reality (VR).

AI is changing the MS&T Landscape:

TOPIC: Impact of AI on Medical Imaging Devices and NVIDIA’s Quadro RTX Roadmap

PRESENTERS: Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY Technologies, and Jeff Krueger, Director of Systems Engineering, Dedicated Computing


Click Here to Watch this Webcast On-Demand

Webcast: The Impact of Artificial Intelligence on Medical Devices

Artificial Intelligence in Healthcare is fundamentally changing how the world develops, deploys, and delivers compute intensive digital services and solutions. The impact of AI, deep learning and machine learning will be far greater than we can predict.  Andrew Ng, Baidu’s former Chief Scientist, and a Stanford Adjunct Professor, noted, “Artificial intelligence is the new electricity.”  We can say with confidence that utilizing AI is a business imperative that spans industries and markets planet wide.

This webinar series addresses how artificial intelligence in healthcare is forcing system integrators to address key challenges as the AI-revolution continues to advance and evolve at an increasing rate.

GPU Enhanced Medical Imaging Systems

Medical device design manufacturers continuously face regulatory challenges in addition to customer demands for improved performance, better features, improved image quality, and lowered acquisition costs. This webinar highlights how medical device manufacturers are utilizing GPUs to enable next generation imaging systems with AI enhanced diagnostic solutions to assist healthcare practitioners and provide better patient outcomes.

TOPIC: Impact of AI on Medical Imaging Devices and NVIDIA’s Quadro RTX Roadmap

PRESENTERS: Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY Technologies, and Jeff Krueger, Director of Systems Engineering, Dedicated Computing


Click Here to Watch this Webcast On-Demand



Competing in Medical Device Design: Gain an Edge with a Smarter Look at TCO

Medical System Development Costs are Better Defined by Lifecycle Management, Last Time Buys, and Long-Term Component Availability

In the challenging and diverse realm of medical system design, not only do regulating agencies demand quality and compliance, but patients and doctors expect smart, connected health strategies. Systems must perform reliably in the spectrum of mission-critical healthcare environments, which may mean hospitals, doctors’ offices, in the home, or emergency settings. Global factors such as an aging population and the need for healthcare anywhere create even greater impact, keeping the market dynamic and continually evolving.

For manufacturers of healthcare medical devices, delivering on the promise of availability is just the beginning. Keeping systems in the field as long as possible is crucial for patient treatment as well as market leadership. Strategies for longevity must include both a design and support perspective and may require original equipment manufacturers (OEMs) to challenge the status quo.

Use Case: A start-up medical manufacturer scales change management

One medical design start-up, a developer of systems for fluorescence image-guided surgery, recognized smarter change management as a way to compete and win. Cashflow and reduced costs were critical to success, driving its focus on components with a strong balance of performance and longevity. In this scenario, the product lifecycle had to prove viable for long-term medical deployment by offering cost-effective, FDA-validated performance that would interest a buyer for the overall company.

This OEM recognized that any conversation around lifecycle management must also include total cost of ownership (TCO) and how it is defined. Once the team established a timeline for system development, they evaluated TCO as a complete picture. In this case, the OEM prioritized the need for insight into when component replacement should be expected and the associated cost.

This is a shift from a traditional look at TCO. Instead of simply outlining upfront system costs – and bidding supplier against supplier for pure component costs – the OEM sought proposals based on lifecycle management, last time buys, and long-term component availability. Newly empowered to evaluate system TCO over a full ten-year period, the OEM chose suppliers committed to meeting long-term needs and offering the best total cost.

To optimize planning and align its hardware Bill of Materials (BOM) with program needs, the OEM considered which software elements were right for its stack. This critical step acknowledges the software stack must not only accommodate the application software but must also be managed in the same way as the hardware stack. In addition, the OEM considered the lifecycle of each operating system (OS), which may affect system functionality and add validation cycles to system design.

In this scenario, the OEM recognized that evaluating and choosing an OS is equally as important as choosing ideal hardware components. Making a change to a piece of hardware impacts system certifications, but the OEM went further and considered whether it also affects the software image that goes on that device. Because upgrading and re-validating the software image presents a significant undertaking, this OEM embraced and anticipated the challenge, planning for it at the early stages of design. As a result, changes to hardware and software were slated to occur in tandem in the design or upgrade cycle. Re-validation is still significant but orchestrating it as a unified effort played a role in streamlining costs and engineering resources. The OEM saw competitive value by avoiding multiple re-validations that add cost and time to overall product resources.

Artificial Intelligence: Get on Board or Get out of the Way

Artificial intelligence – machines’ ability to learn on their own – promises to change the way business is done.

Dedicated Computing Engineers attending MSOE’s grand opening ceremonies of the new Diercks Computational Science Hall

“It really is a new kind of industrial revolution, ” says Derek Riley, Ph.D., computer science program director at Milwaukee School of Engineering (MSOE). “Much the way assembly lines revolutionized how things were made, AI technology is going to change the way people interact with computers, other people, and just about everything.”

Artificial intelligence applications have been around for more than a decade, but their use is reaching a tipping point due to advances in computing technology, the massive amounts of data that have been compiled, and the development of algorithms that are now sophisticated enough to comb through data to identify patterns and solve real problems.

“AI is all about streamlining decision making,” Riley says. “It’s about letting humans do what humans are good at and letting computers do what computers are good at.” Jeff Krueger, director, systems engineering at Waukesha-based Dedicated Computing, agrees. “Data analytics, machine learning, and artificial intelligence are changing how diseases are diagnosed,” he says. “Students training through simulators will have the ability to dynamically adjust to the learning techniques of the individual, creating deeper and engaging environments that will increase overall learning.”

Dedicated Computing designs, develops, tests, and manufactures application-specific, high-performance computational solutions used in life sciences, healthcare, training and simulation, and Industry 4.0. It is sponsoring the Dedicated Computing Data Analytics Laboratory – a data-rich, reconfigurable laboratory space – in MSOE’s new Dwight and Dian Diercks Computational Science Hall.

Krueger says MSOE’s new lab will be critical for giving people the skills needed to fully leverage artificial intelligence.

“For many years, sensors, data collection endpoints and IT systems have logged an incredible amount of data, most of it dark – which means it is stored, but has not been looked at or processed,” he says. “Embedded in this data are potentially patentable ideas, as well as new and unique methods by which a product can surpass the competitors. Data analytics and increased computational capability will make large and small businesses more nimble, allowing them to create impactful and beneficial solutions.” Riley agrees. “In order to be effective, small companies have to understand the general ideas of how these technologies work, so they can make good business decisions around where they should use an AI solution to address a problem,” he says.

MSOE is at the forefront of bringing artificial intelligence into the mainstream.

“Our focus on AI is pretty unique,” Riley says. “The only two schools that I can point to that are doing something in the same vein are the Massachusetts Institute of Technology, which recently realigned their computer science department, and Carnegie Mellon University. Both are much bigger, research-focused universities. What makes us unique is that we are focused on undergraduate students and applied skills.”

“What MSOE is doing is fantastic for Wisconsin, the Midwest, and all those impacted by the migration of knowledge across all domains,” says Krueger. “Investing in the infrastructure, resources, and staff will benefit Wisconsin, as well as current and future students. We hope to aid in increasing the awareness of technology, methods, and our experience in developing and managing similar solutions.”

This article was originally published as a supplement to the Milwaukee Business Journal, Sept. 13, 2019

Dedicated Computing & MSOE – A Bold Step Taken

New 64,000 sq. ft. computational facility will focus on artificial intelligence, machine learning, robotics, and cybersecurity.

DC Engineers at MSOE

Senior Mechanical Engineer, Qilu He, showcases his projects at Dedicated Computing with visitors at the Diercks Computational Science Hall grand opening.

Dedicated Computing recently unveiled the DC Data Analytics Laboratory as part of their on-going partnership with the Milwaukee School of Engineering (MSOE). The unveiling was in conjunction with MSOE’s grand opening ceremonies for the new Dwight and Dian Diercks Computational Science Hall. The Dedicated Computing Data Analytics Laboratory is a data-rich, reconfigurable laboratory which can evolve with the university’s AI-centric curriculum.

DC also announced a Co-Working Internship with MSOE, which provides students the opportunity to apply concepts in real-world situations.

“Dedicated Computing values the collaborative spirit at MSOE,” says Jeff Krueger, Director of System Engineering at Dedicated Computing. “Partnering with MSOE is about learning, growing and benefiting from each other’s knowledge and experience. Diercks Hall specifically provides the ability to harness the power of AI through a partner approach to enhancing business capability. Our work with MSOE’s emerging technology leaders increases our adaptability in the evolution of solutions embodying AI to cure disease, advance military training, and evolve industrial automation systems.”

For more reporting on the grand opening event and Diercks Hall, follow the links below:

MSOE Corporate Partnership

NVIDIA Software Head Helps Transform Alma Mater into Leading AI Center with $34M Gift

MSOE Official Announcement

Extending Security and Reliability: Excellence in Medical Device Support Blends Remote and Onsite Strategies

Proactive Service Demands Real-time Insight Into Medical Device Performance, Balances Costs and Risk

Keeping healthcare systems in the field as long as possible is an exceptional demand for original equipment manufacturers (OEMs). Support strategies can play a critical role, recognizing security as a primary design requirement for connected devices. These concerns must be considered at the earliest stages of development rather than ‘bolted’ on later — which requires the understanding of implementation logistics and goals for achieving the right level of security – acceptable to the manufacturer as well as the device’s operational setting.

Security features can also be built into the software image, allowing systems to be online and less susceptible to security breaches. Early discussions, in advance of the software image being designed, can allow for a greater understanding of security features that may help the system achieve a higher threshold of protection while maintaining connectivity.

Use case: Early planning improves reliability and addresses security with a smart blend of remote and onsite support

One OEM of medical device equipment recognized a key support issue with its systems being returned noted as “no issue found.” Diagnosis was failing because the system was being removed from its environment and the repair depot could not replicate the problem. Remote options added great value here, as the manufacturer’s systems didn’t need to be removed from the field and could be diagnosed in their functional environment.

For this OEM, remote monitoring required connectivity, empowering the system operator with real-time insight on performance issues and system health. This facilitates proactive service, preventing failures before they happen – as well as highly focused service, tapping into better data to reduce costs for unnecessary service calls. Software updates can be handled remotely as well, eliminating the need for service techs to apply patches onsite using physical devices such as USB keys – for a significant cost savings.

At the same time, connectivity flies in the face of security concerns, potentially increasing vulnerability and risk that healthcare providers are keen to reduce and avoid. OEMs must base consideration of the preferred option on costs, comfort level and requirements of the deployment setting – as well as the likelihood of risk. For example, this OEM considered a hospital environment with a number of different infrastructure layers to navigate. In these types of scenarios, policies must be followed even as they vary from facility to facility. In some cases, security protocols may not even allow an IoT-enabled system to function as a connected device; early discussion of these risks and limitations is critical to a balanced and successful support strategy.

Prioritizing lifecycle for a healthy competitive edge

Reliable, long-term performance is crucial in medical equipment design. Nothing is more important to patients than acquiring reliable access to lifesaving treatment. And nothing is more powerful in establishing OEM leadership than creating a reputation for longevity of system performance. For greater insight on how Dedicated Computing is enabling the competitive vision and strategy of global healthcare OEMs, request a meeting with an engineer here.