Masimo secures FDA clearance for neonatal RD SET Pulse Oximetry sensors with improved accuracy specifications

Masimo announced that RD SET sensors with Masimo Measure-through Motion and Low Perfusion SET pulse oximetry have received FDA clearance for improved oxygen saturation (SpO2) accuracy specifications for neonatal patients (< 3 kg). 

The updated RD SET sensors’ SpO2 accuracy specifications have improved significantly, from 3 percent to 1.5 percent ARMS (at 1 standard deviation), in conditions of motion and no motion, providing clinicians with even greater confidence when monitoring the oxygenation status of neonates. With this clearance, the improved performance specifications, which were incorporated into RD SET sensors for patients > 3 kg in 2018, are now available to all patient populations in the United States.

Masimo’s SET pulse oximetry has been shown in more than 100 independent and objective studies to outperform other pulse oximetry technologies – even before the revisions that achieved the improved accuracy specifications – providing clinicians with increased sensitivity and specificity to help them make critical patient care decisions. 

Crucially for newborn health, SET has been shown to help clinicians reduce severe retinopathy of prematurity in neonates3 and in multiple studies, including the largest critical congenital heart disease (CCHD) study to date, to improve CCHD screening in newborns.

In addition to offering improved accuracy, RD SET sensors are designed to enhance patient comfort, optimize clinician workflows, and help hospitals meet green initiatives. 

The sensors are lightweight and have a flat, soft cable with smooth edges, so that they lie comfortably on a patient’s hand or foot. In particular, RD SET NeoPt sensors with Velaid SofTouch use little to no adhesive, facilitating quick but gentle application and repositioning on the fragile skin of newborns and pre-term babies. 

RD SET sensors also feature an intuitive sensor-to-cable connection, while their lightweight design results in up to 84 percent less waste and their sleek, recyclable packaging reduces storage and shipping space.

“We’re delighted to announce the latest result of our continued innovation in our foundational SET pulse oximetry. We have long been dedicated to helping improve the lives of neonatal, infant, and pediatric patients, and this clearance significantly furthers that mission,” said Joe Kiani, founder and CEO of Masimo. “Thanks to the brilliance and dedication of our engineers and the continuing support of our customers, we’ve been able to once again raise the standard for pulse oximetry performance. Even though no one has been able to create pulse oximetry that outperforms SET, we have not allowed that to stop us from continuing our pursuit of perfecting pulse oximetry.”

NetApp predicts data management and IT trends will lead in the New Year

2019 was a year of rapid innovation—and disruption—for both the IT industry and the broader business community. With the widespread adoption of hybrid multicloud as the de facto architecture for enterprise customers, organizations everywhere are under tremendous pressure to modernize their infrastructure and to deliver tangible business value around data-intensive applications and workloads.

As a result, organizations are shifting from on-premises environments to using public cloud services, building private clouds, and moving from disk to flash in data centers—sometimes concurrently. These transformations open the door to enormous potential, but they also introduce the unintended consequence of increased IT complexity.

NetApp predicts that a demand for simplicity and customizability will be the number one factor that drives IT purchasing decisions in 2020. Vendors will need to offer modern, flexible technologies with the choice of how to use and to consume those technologies so that customers can keep pace with their evolving business models. As IT departments strive to deemphasize maintenance and hardware, to reduce overhead, and to adopt pay-as-you-go models, simplicity and choice will be crucial.

Achieving this simplicity will serve as the foundation for companies as they navigate the exciting technological trends that we identify in the following sections.

1. As the advent of 5G makes AI-driven Internet of Things (IoT) a reality, edge computing environments are primed to become even more disruptive than cloud was.

In preparation for the widespread emergence of 5G, lower-cost sensors and maturing AI applications will be used to build compute-intensive edge environments. This effort will lay the groundwork for high-bandwidth, low-latency AI-driven IoT environments with the potential for huge innovation—and disruption.

The advent of 5G is what AI-driven IoT has been waiting for. It will take a few more years for 5G data technology to spread across the entire United States. However, 2020 will see many players in the technology industry and business community invest in building edge computing environments to support the reality of AI-driven IoT. These environments will make possible new use cases that rely on intelligent, instantaneous, and autonomous decision-making, with low-latency, high-bandwidth capabilities. This evolution will bring us to a world where the internet will work on our behalf—without even having to ask.

This AI-driven IoT innovation, however, will depend on a massive prioritization of edge computing, further disrupting IT infrastructures and data management priorities. As edge devices move beyond home devices (such as connected thermostats and speakers) and become more far-reaching (such as connected solar farms), more data centers will be placed at the edge. Also, platforms such as artificial intelligence for IT operations (AIOps) will be necessary to help monitor complex environments across the edge, the core, and the cloud.

2. The impact of blockchain will be undeniable as indelible ledgers rapidly enable game-changing use cases outside of cryptocurrency.

The world is quickly moving beyond Bitcoin to adopt enterprise-distributed indelible ledgers, setting the stage for a transformation that’s exponentially bigger than the impact that cryptocurrency has had on blockchain in finance. 

While the crypto frenzy continues to steal the limelight when it comes to blockchain, most players in the industry understand the bigger picture of the technology and its potential. Going into 2020, we will see a tipping point for larger implementations as enterprises go a step further to adopt indelible ledgers based on Hyperledger, which represents the maturation of blockchain for wider use cases. Indeed, we will start to see blockchain go “mainstream” as it enables industries such as healthcare to create universal patient records, to improve chain-of-custody pharmaceutical processes, and more.

With such use cases validating blockchain and indelible ledgers, additional widespread adoption of the technology will drive transformation across society on a larger scale. This widespread adoption will build on the disruption that cryptocurrency has brought to finance to touch nearly every industry. As a result, new data management and compute capabilities will encourage companies to invest in indelible ledgers to build differentiated applications and to collaborate on critical, sensitive datasets.

3. Hardware-based composable architecture will have less short-term potential against commodity hardware and software-based infrastructure virtualization.

Continued improvements in commodity hardware performance, software-based virtualization, and microservice software architectures will eliminate much of the performance advantage of proprietary hardware-based composable architectures, relegating them to niche data center roles soon. 

Hardware-based composable architecture is being hyped as the next evolution of hyperconverged infrastructure (HCI). This architecture enables CPUs, networking cards, workload accelerators, and storage resources to be distributed across a rack-scale architecture and to be connected with low-latency PCIe-based switching. 

Although composable architecture does have potential, standardization has been slow, and adoption has been even slower. Meanwhile, software-based virtualization of storage, combined with software-based (but hardware-accelerated) compute and networking virtualization solutions, offers much of the flexibility of hardware-based composable architectures today with lower cost and consistently increasing performance.

Next year, attempts to build a true hardware-based rack-scale computing model will no doubt continue, and the space will continue to evolve quickly. However, most organizations that must transform within 2020 will be best served by a combination of modern HCI architectures (including disaggregated HCI) and software-based virtualization and containerization.

Trend Micro reports on Microsoft discovering polymorphic malware ‘Dexphot’ that affected 80,000 Windows systems

For over a year, Microsoft has been monitoring a malware strain they named “Dexphot” that has been infecting Windows devices since October last year, Trend Micro revealed in a recent post. The malware used computer resources to mine cryptocurrency and profit from the attack. It reached its peak in June 2019, infecting almost 80,000 computers before gradually decreasing over the next months because of Microsoft’s intervention.

Despite the typical malware payload, Microsoft claimed that monitoring the Dexphot gave them insight into not only on how the malware worked but also the techniques that cybercriminals currently use.

This was largely because of the way Dexphot behaved over the course of last year, as noted by Microsoft. The simple payload was delivered through complex techniques that were constantly updated by the malicious actors behind the malware strain.

Microsoft found that the Dexphot malware strain was dropped by another malware known as ICLoader, which is unknowingly installed on a user’s system as part of software bundles. Dexphot was found downloaded and installed in Windows systems that were infected by ICLoader.

While Microsoft Defender Advanced Threat Protection’s pre-execution detection engines blocked Dexphot in most cases, behavior-based machine learning models provided protection for cases where the threat slipped through. Given the threat’s persistence mechanisms, polymorphism, and use of fileless techniques, behavior-based detection was a critical component of the comprehensive protection against this malware and other threats that exhibit similar malicious behaviors.

Microsoft Defender ATP data shows the effectiveness of behavioral blocking and containment capabilities in stopping the Dexphot campaign. Over time, Dexphot-related malicious behavior reports dropped to a low hum, as the threat lost steam.

Dexphot used legitimate system processes for its malicious activities. It used legitimate Windows apps such as msiexec.exe, unzip.exe, rundll32.exe, schtasks.exe, and powershell.exe to decrypt its data files. Using such tools allows Dexphot to evade detection, as the system would consider its activities as normal processes.

In addition, the decrypted files contained three executable files which are never written on filesystem. They remain on memory. This means Dexphot also used fileless techniques.

Dexphot instead laces the first two executable files into other legitimate system processes like svchost.exe or nslookup.exe. These are monitoring services that maintain Dexphot components. Finally, it replaces setup.exe contents with its third executable, a cryptocurrency miner.

Microsoft saw that Dexphot switched miners throughout their monitoring, using both programs like XMRig and JCE.

Microsoft noted that Dexphot was a malware strain that was not likely to garner much attention for its common payload. However, it does paint a good picture of the techniques that had been pervasive throughout this year, namely living off the land and fileless techniques.

Trend Micro’s most recent security roundup reported that threat actors have been increasingly living off the land. In fact, detections for fileless threats was 18 percent higher during the first half of 2019 compared to the total count for 2018.

Remaining vigilant and wary of similar cases as Dexphot can help in defending against fileless threats moving forward. Organizations would need to consider solutions like behavioral indicators and traffic monitoring to defend against the unique challenges that fileless threats present.

Trend Micro's Smart Protection Suites deliver several capabilities like high-fidelity machine learning and web reputation services that minimize the impact of persistent, fileless threats. 

Trend Micro Apex One protection employs a variety of threat detection capabilities, notably behavioral analysis that protects against malicious scripts, injection, ransomware, memory and browser attacks related to fileless threats. Additionally, the Apex One Endpoint Sensor provides context-aware endpoint investigation and response (EDR) that monitors events and quickly examines what processes or events are triggering malicious activity. 

The Trend Micro Deep Discovery solution has a layer for email inspection that can protect enterprises by detecting malicious attachments and URLs. Deep Discovery can detect remote scripts even if it is not being downloaded in the physical endpoint.

Amazon Web Services updates its AWS DeepRacer program with new sensing capabilities and training algorithms

Amazon Web Services (AWS) announced on Wednesday that it is upgrading its DeepRacer program by adding more chances to compete at AWS events & at own events, more chances to win, with new races including head-to-head multi-car competitions, and an upgraded DeepRacer car with new sensing capabilities.

AWS DeepRacer is an autonomous 1/18th scale race car designed to test RL models by racing on a physical track. Using cameras to view the track and a reinforcement model to control throttle and steering, the car shows how a model trained in a simulated environment can be transferred to the real-world.

AWS DeepRacer Evo is the next generation in autonomous racing. Take your car to the tracks and master brand new AWS DeepRacer challenges including racing head to head against other cars in the 2020 season of the AWS DeepRacer League. AWS DeepRacer Evo comes with dual stereo cameras, allowing the car to detect objects on the track, and LIDAR (light detection and ranging) helping to determine when to overtake another car and beat it to the finish line.

AWS DeepRacer offers hands-on experience with Reinforcement Learning (RL). Following launch of the AWS DeepRacer car and the AWS DeepRacer League, users could have the opportunity to get experience and new skills in a fun, competitive environment. In less than a year, tens of thousands of developers have participated in hands-on and virtual races located globally. 

The upcoming AWS DeepRacer Evo car will include a stereo camera and a Light Detection and Ranging (LIDAR) sensor. The added sensors will enable DeepRacer Evo to skillfully detect and respond to obstacles, including other DeepRacers. This will help users learn more about the field of reinforcement learning, which is ideal for use in autonomous driving.

The new sensors will be available soon in virtual form for use in the new My Garage section of the DeepRacer Console. AWS will release more details on production plans for AWS DeepRacer Evo, including a sensor upgrade kit for existing DeepRacer car, early next year.

AWS is also expanding the DeepRacer League in 2020. The company is adding eight additional races across five countries as part of an expanded AWS Summit presence, and 18 additional virtual races. There will also be a track (and a race) at re:MARS 2020. As a result, customers have the opportunity to participate in 30 events and join for an in-person AWS DeepRacer League Summit race, along with 24 Virtual Circuit races from anywhere in the world.

In addition to the existing time trial race, AWS is adding two new race types to give some new RL challenges, and the opportunity to experiment with different sensors. The object detection and avoidance features uses the sensors to detect and (hopefully) avoid obstacles, and the head-to-head racing against another DeepRacer that is on the same track. 

Sony Electronics adds APIs to its aibo robotic companion; delivers further customization and feature enhancements

Sony Electronics has announced an extensive software update for its autonomous robot puppy companion, aibo. The software update, Version 2.50 for the ERS1000 aibo model, opens the device to developer customization and feature enhancements, reflecting Sony's ongoing commitment to the continued evolution of aibo. 

Both U.S. and Japanese aibo owners now have access to exciting new programmable resources with the update, as well as several new whimsical capabilities, such as the ability to virtually feed aibo cookies, potty train it and more. 

The Version 2.50 software update introduces a new aibo web-based application programming interface (API), which provides owners access to both the "aibo Developer Program" for more seasoned developers and the "aibo Visual Programming" for beginner programmers. The web-based API can be used free of charge and allows owners to program aibo's actions. aibo's personality cannot be altered by any web API programming, so while users can program aibo to perform different actions, they cannot change the emotion, character or mood of aibo through programming.

Sony is looking to developers to continue building on aibo's abilities and create greater possibilities for aibo in the future with the aibo Developer Program. It is a licensing program that is accessible through a Web API to any aibo owner with the desire to create new applications, traits and experiences for alibi. 

Developers can utilize this program to create services and applications that can be linked with aibo. The mechanism of this API is the same as a general REST API, and it can be implemented in almost any system with an Internet environment. 

aibo Visual Programming is an easy-to-use tool that allows owners to create original movements and tricks for aibo. The tool features a drag and drop block coding user interface for beginners making it easy to utilize. 

By encouraging programming and development through the Web API, Sony Electronics aims to expand and promote collaboration with products and services provided by various creators, companies, organizations and educational institutions. In the future, Sony plans to enable developers who use aibo's software API to provide cooperative applications that can be shared by aibo users. 

In addition to the programmable options made available through the API, Sony has included other new enhancements for aibo owners. Now, owners can have virtual fun feeding aibo through the My aibo App using the new mealtime feature, "aibo Food." 

The aibo Food feature can be enjoyed for free using "bonus coins," which can be exchanged for meals called "aibocrisps." Owners can earn "bonus coins" by signing into the My aibo App at specific intervals or during special events. Should an aibo owner run out of coins and wish to virtually feed aibo more frequently, more coins can always be purchased for a fee via the My aibo app.  

Syncron Uptime strengthens manufacturers’ transition from after sales-service to products-as-a-service

Syncron launched its Syncron Uptime offering that uses Machine Learning and Artificial Intelligence to analyze real-time sensor data, predict failures, prescribe optimized maintenance actions and ultimately maximize product uptime.

Servitization, which is the transformation from selling products to selling products-as-a-service, has ushered in a new generation of customers that prefers access over ownership. This increasingly popular consumption preference is driving original equipment manufacturers (OEMs) to shift from product-centric to service-centric business models.

When implementing an after-sales service strategy that is centered on maximized product uptime, OEMs commonly encounter a small minority of the overall mechanical failures that contribute to unplanned downtime follow a pattern that is correlated to time and/or usage, while the overwhelming majority are random occurrences. 

As a result, present approach to preventive maintenance adds to increased total cost of ownership without effectively improving availability and uptime. The only way to improve product uptime without increasing risk, plus improving cost effectiveness, is to look for subtle symptoms and pre-cursors leading up to the point of failure.

To overcome random, symptom-based failures, OEMs have been investing heavily in sensors and IoT, collecting massive amounts of data. However, it is impossible to manually manage or analyze this data in any beneficial way. The only solution is to leverage modern machine earning and artificialiIntelligence technologies – mathematical algorithms that can find the subtle patterns to provide the earliest possible indications of anomalies and failure patterns.

Manufacturers are focused on designing products that are easier and safer to operate. However, this has led to increased complexity when it comes to servicing and maintaining newer equipment. Simultaneously, the baby boomer generation is retiring and taking critical, expert knowledge with them, leaving OEMs struggling to find trained service technicians and engineers who can effectively troubleshoot and diagnose problems based on early symptoms, which can lead to a reduction in first-time fix rates.

Manufacturers’ organizational structures are designed for product-centric approach, treating sales of products, spare parts and service as separate functions, which has led to disparate IT systems and KPIs. Servitization, however, requires a new way of thinking where accountability does not end after a product sale, instead continuing throughout a product’s entire lifecycle. OEMs will be contract-bound for pre-defined performance measures that carry incentives and penalties based on customer outcomes.

Syncron Uptime has been designed to overcome these challenges, enabling OEMs to maximize product uptime cost effectively. With this new, enterprise software solution users can combine OEMs’ investments in IoT and sensor data with machine learning and artificial intelligence to detect anomalies and predict failures, leading to optimized maintenance for every complex machine in the field and improving product uptime and maintenance costs. 

It also captures knowledge and best practices for troubleshooting products with the earliest and smallest indications of deterioration in performance, increasing field service productivity and improving first-time fix rates.

The offering breaks down data and organizational siloes to enable coordination between operations, field service management and service parts planning, providing a single source of truth to all stakeholders. It also integrates with predictive maintenance work requests with field service systems and Computerized Maintenance Management System (CMMS) to make the best decisions for both the business and end customers, while maximizing product uptime and improving the overall customer experience.