Maximize cooling performance to protect critical systems from damaging heat
Post Syndicated from Mercer original https://spectrum.ieee.org/at-work/education/the-growing-health-and-economic-crisis-of-chronic-conditions-in-the-us
These health conditions are driving up healthcare costs. Here’s how a new IEEE member benefit can help prepare for them
In the next five years, it is estimated that ongoing, chronic illnesses or conditions like heart disease, cancer, stroke, arthritis, and diabetes, among others, will impact nearly half the U.S. population and be responsible for 7 out of every 10 deaths.1 Currently, 60% of Americans are living with one chronic condition; 40% with two or more.2
People with chronic conditions are the primary users of healthcare services: they account for 81% of hospital admissions; 91% of all prescriptions filled; and 76% of all physician visits.1 Chronic conditions contribute to 90% of all healthcare spending3 (99% of Medicare spending1) and are the leading cause of the significant increases in healthcare costs in the U.S.
Healthcare premiums for employer-sponsored family coverage have increased 87% since 2000.1
Healthcare costs for people with a chronic condition are five times higher ($7,900 annually) than for those without a condition.4
Average deductible and coinsurance payments increased 176% and 67%, respectively, over a 10-year period and out-of-pocket spending rose 54%.5
While chronic conditions are driving up the costs and usage of healthcare, they are often preventable.
The U.S. Centers for Disease Control and Prevention estimates1 that eliminating these three risk factors — poor diet, inactivity, and smoking — would prevent:
80% of heart disease and stroke.
80% of type 2 diabetes.
40% of cancer.
Regardless of age, you can act and make lifestyle changes to help prevent a chronic condition from happening to you. Now is also a good time to start preparing financially in case such a condition impacts your family.
Your basic health insurance covers many of the medical and treatment costs associated with a chronic condition. But most policies have deductibles, limitations, and benefit maximums that could become expensive with an ongoing chronic condition. For this reason, insurance carriers are beginning to offer chronic illness insurance policies and riders to help offset out-of-pocket expenses.
Recently, to evolve with the changing health and lifestyle needs of its members, IEEE negotiated to add a chronic illness rider to its IEEE Member Group Term Life Insurance Plan.
Based on member feedback and concerns about the financial impact of long-term chronic illness and the need for all-inclusive coverage, IEEE backed this particular benefit because it offered broad coverage for a variety of chronic conditions, unlike some plans that may limit coverage to one or a few specific illnesses or diseases.
With this rider, insured individuals under age 80 can accelerate some of their term-life insurance benefits if they qualify for chronic illness benefits. These benefits can be used to pay for medical or other expenses they choose.
The chronic illness rider is available to IEEE Members and spouses under age 65 residing in the U.S., (excluding Connecticut, Idaho, Louisiana, Minnesota, Montana, North Carolina, Ohio, South Dakota, Utah, and Washington).
Visit www.ieeeinsurance.com for more details.*
This information is provided by the IEEE Member Group Member Insurance Program Administrator, Mercer Health & Benefits Administration, LLC, in partnership with IEEE to provide IEEE Members with important insurance, health and lifestyle information.
*Including features, costs, eligibility, renewability, limitations, and exclusions.
The IEEE Member Group Term Life Insurance Plan is available in the U.S. (except territories), Puerto Rico and Canada (except Quebec). This plan is underwritten by New York Life Insurance Company, 51 Madison Ave., New York, NY 10010 on Policy Form GMR
The IEEE Member Group Insurance Program is administered by:
Mercer Health & Benefits Administration LLC, 12421 Meredith Drive, Urbandale, IA 50398
In CA d/b/a Mercer Health & Benefits Insurance Services LLC, AR Insurance License #100102691 CA Insurance License #0G39709, 87572 (5/19) Copyright 2019 Mercer LLC. All rights reserved.
1. The Growing Crisis of Chronic Disease in the United States, Partnership to Fight Chronic Disease
2. About Chronic Diseases, Center for Disease Control
3. Health and Economic Costs of Chronic Diseases, Center for Disease Control
4. “The Rising Cost of Healthcare by Year and Its Causes,” Kimberly Adadeo. The Balance
5. “Increases in cost-sharing payments continue to outpace wage growth,” Gary Claxton, Larry Levitt, Matthew Rae and Bradley Sawyer; Kaiser Family Foundation. Peterson-Kaiser Health System Tracker
Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/5g-terms-and-acronyms-defined
Want to sound like a 5G expert?
With so many 5G terms talked about these days, it’s easy to get confused. Even more terms and acronyms are on the way. We’ve got you covered with our up-to-date publication quality list of over 90 terms, ranging from AM Distortion to Xn Interface. This handy resource is sure to make you sound like a 5G expert and is perfect for sharing with your colleagues or students.
Post Syndicated from LEMO original https://spectrum.ieee.org/robotics/industrial-robots/robotic-animal-agility
Packed with sensory systems and equipped with revolutionary joints, the ANYmal robot is perfectly at ease on even the roughest terrain. It will soon be ready to inspect industrial sites, sewage systems and agricultural fields with complete autonomy.
An off-shore wind power platform, somewhere in the North Sea, on a freezing cold night, with howling winds and waves crashing against the impressive structure. An imperturbable ANYmal is quietly conducting its inspection.
ANYmal, a medium sized dog-like quadruped robot, walks down the stairs, lifts a “paw” to open doors or to call the elevator and trots along corridors. Darkness is no problem: it knows the place perfectly, having 3D-mapped it. Its laser sensors keep it informed about its precise path, location and potential obstacles. It conducts its inspection across several rooms. Its cameras zoom in on counters, recording the measurements displayed. Its thermal sensors record the temperature of machines and equipment and its ultrasound microphone checks for potential gas leaks. The robot also inspects lever positions as well as the correct positioning of regulatory fire extinguishers. As the electronic buzz of its engines resumes, it carries on working tirelessly.
After a little over two hours of inspection, the robot returns to its docking station for recharging. It will soon head back out to conduct its next solitary patrol. ANYmal played alongside Mulder and Scully in the “X-Files” TV series*, but it is in no way a Hollywood robot. It genuinely exists and surveillance missions are part of its very near future.
Off-shore oil platforms, the first test fields and probably the first actual application of ANYmal. ©ANYbotics
This quadruped robot was designed by ANYbotics, a spinoff of the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Made of carbon fibre and aluminium, it weighs about thirty kilos. It is fully ruggedised, water- and dust-proof (IP-67). A kevlar belly protects its main body, carrying its powerful brain, batteries, network device, power management system and navigational systems.
ANYmal was designed for all types of terrain, including rubble, sand or snow. It has been field tested on industrial sites and is at ease with new obstacles to overcome (and it can even get up after a fall). Depending on its mission, its batteries last 2 to 4 hours.
On its jointed legs, protected by rubber pads, it can walk (at the speed of human steps), trot, climb, curl upon itself to crawl, carry a load or even jump and dance. It is the need to move on all surfaces that has driven its designers to choose a quadruped. “Biped robots are not easy to stabilise, especially on irregular terrain” explains Dr Péter Fankhauser, co-founder and chief business development officer of ANYbotics. “Wheeled or tracked robots can carry heavy loads, but they are bulky and less agile. Flying drones are highly mobile, but cannot carry load, handle objects or operate in bad weather conditions. We believe that quadrupeds combine the optimal characteristics, both in terms of mobility and versatility.”
What served as a source of inspiration for the team behind the project, the Robotic Systems Lab of the ETH Zurich, is a champion of agility on rugged terrain: the mountain goat. “We are of course still a long way” says Fankhauser. “However, it remains our objective on the longer term.
The first prototype, ALoF, was designed already back in 2009. It was still rather slow, very rigid and clumsy – more of a proof of concept than a robot ready for application. In 2012, StarlETH, fitted with spring joints, could hop, jump and climb. It was with this robot that the team started participating in 2014 in ARGOS, a full-scale challenge, launched by the Total oil group. The idea was to present a robot capable of inspecting an off-shore drilling station autonomously.
Up against dozens of competitors, the ETH Zurich team was the only team to enter the competition with such a quadrupedal robot. They didn’t win, but the multiple field tests were growing evermore convincing. Especially because, during the challenge, the team designed new joints with elastic actuators made in-house. These joints, inspired by tendons and muscles, are compact, sealed and include their own custom control electronics. They can regulate joint torque, position and impedance directly. Thanks to this innovation, the team could enter the same competition with a new version of its robot, ANYmal, fitted with three joints on each leg.
The ARGOS experience confirms the relevance of the selected means of locomotion. “Our robot is lighter, takes up less space on site and it is less noisy” says Fankhauser. “It also overcomes bigger obstacles than larger wheeled or tracked robots!” As ANYmal generated public interest and its transformation into a genuine product seemed more than possible, the startup ANYbotics was launched in 2016. It sold not only its robot, but also its revolutionary joints, called ANYdrive.
Today, ANYmal is not yet ready for sale to companies. However, ANYbotics has a growing number of partnerships with several industries, testing the robot for a few days or several weeks, for all types of tasks. Last October, for example, ANYmal navigated its way through the dark sewage system of the city of Zurich in order to test its capacity to help workers in similar difficult, repetitive and even dangerous tasks.
Why such an early interest among companies? “Because many companies want to integrate robots into their maintenance tasks” answers Fankhauser. “With ANYmal, they can actually evaluate its feasibility and plan their strategy. Eventually, both the architecture and the equipment of buildings could be rethought to be adapted to these maintenance robots”.
ANYmal requires ruggedised, sealed and extremely reliable interconnection solutions, such as LEMO. ©ANYbotics
Through field demonstrations and testing, ANYbotics can gather masses of information (up to 50,000 measurements are recorded every second during each test!) “It helps us to shape the product.” In due time, the startup will be ready to deliver a commercial product which really caters for companies’ needs.
Inspection and surveillance tasks on industrial sites are not the only applications considered. The startup is also thinking of agricultural inspections – with its onboard sensors, ANYmal is capable of mapping its environment, measuring bio mass and even taking soil samples. In the longer term, it could also be used for search and rescue operations. By the way, the robot can already be switched to “remote control” mode at any time and can be easily tele-operated. It is also capable of live audio and video transmission.
The transition from the prototype to the marketed product stage will involve a number of further developments. These include increasing ANYmal’s agility and speed, extending its capacity to map large-scale environments, improving safety, security, user handling and integrating the system with the customer’s data management software. It will also be necessary to enhance the robot’s reliability “so that it can work for days, weeks, or even months without human supervision.” All required certifications will have to be obtained. The locomotion system, which had triggered the whole business, is only one of a number of considerations of ANYbotics.
Designed for extreme environments, for ANYmal smoke is not a problem and it can walk in the snow, through rubble or in water. ©ANYbotics
The startup is not all alone. In fact, it has sold ANYmal robots to a dozen major universities who use them to develop their know-how in robotics. The startup has also founded ANYmal Research, a community including members such as Toyota Research Institute, the German Aerospace Center and the computer company Nvidia. Members have full access to ANYmal’s control software, simulations and documentation. Sharing has boosted both software and hardware ideas and developments (built on ROS, the open-source Robot Operating System). In particular, payload variations, providing for expandability and scalability. For instance, one of the universities uses a robotic arm which enables ANYmal to grasp or handle objects and open doors.
Among possible applications, ANYbotics mentions entertainment. It is not only about playing in more films or TV series, but rather about participating in various attractions (trade shows, museums, etc.). “ANYmal is so novel that it attracts a great amount of interest” confirms Fankhauser with a smile. “Whenever we present it somewhere, people gather around.”
Videos of these events show a fascinated and sometimes slightly fearful audience, when ANYmal gets too close to them. Is it fear of the “bad robot”? “This fear exists indeed and we are happy to be able to use ANYmal also to promote public awareness towards robotics and robots.” Reminiscent of a young dog, ANYmal is truly adapted for the purpose.
However, Péter Fankhauser softens the image of humans and sophisticated robots living together. “These coming years, robots will continue to work in the background, like they have for a long time in factories. Then, they will be used in public places in a selective and targeted way, for instance for dangerous missions. We will need to wait another ten years before animal-like robots, such as ANYmal will share our everyday lives!”
At the Consumer Electronics Show (CES) in Las Vegas in January, Continental, the German automotive manufacturing company, used robots to demonstrate a last-mile delivery. It showed ANYmal getting out of an autonomous vehicle with a parcel, climbing onto the front porch, lifting a paw to ring the doorbell, depositing the parcel before getting back into the vehicle. This futuristic image seems very close indeed.
*X-Files, season 11, episode 7, aired in February 2018
Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/do-you-know-your-oscilloscopes-signal-integrity
Ebook: How to Determine Oscilloscope Signal Integrity
High oscilloscope signal integrity is critical but often misunderstood! Whether you are debugging your latest design, verifying compliance against an industry standard, or decoding a serial bus, it is important that your oscilloscope displays a true representation of your signal. Learn how to verify that your instrument has the high signal integrity you need with the “How to Determine Oscilloscope Signal Integrity” eBook.
Post Syndicated from National Instruments original https://spectrum.ieee.org/computing/software/university-of-southampton-uses-the-usrp-and-labview-to-change-the-way-it-teaches-wireless-communications
The University of Southampton has been looking at new and innovative ways to teach the principles of wireless communication at a time when there is significant interest in wireless technologies
Demonstrating the Practical Challenges of Wireless Communications
Most electronics education worldwide teaches wireless communications with a typical focus on communications theory. At the University of Southampton, educators have taken a different outlook in teaching students the practical aspects of communication technology to better prepare them for their careers in industry. Students focus on the rapid prototyping of a wireless communications system with live radio frequency (RF) signal streaming for a practical approach to communications education. With this approach, students gain a valuable experience in manipulating live signals for a greater understanding of wireless communication and the associated practical challenges.
A Real Communications System to Demonstrate Practical Concepts
The University of Southampton have accomplished this demonstration of the practical concepts of wireless communication as part of their masters course in wireless communications. The focus was on creating a wireless communications system to demonstrate the concept of differential-quadrature phase-shift keying(DQPSK) and how it is used within wireless communications. The students were given a USRP™ (Universal Software Radio Peripheral) and tasked with building a DPSK transceiver in a practical session. Before this they attended a one-hour lecture on the USRP and how to use it to achieve their learning outcomes. Additionally were given a pre-session assignment to do, which familiarised them with LabVIEW and its environment.
Practical Challenges of Wireless Communication
Southampton students were tasked with building one half of a wireless communications system. The setup consisted of an incomplete DQPSK demodulator, which needed to be completed so that a modulated signal sent by a separate USRP device could be decoded. To complete this task, a number of steps covering different concepts are required so that the end result is a fully working communications system.
The students first applied a filter to the received and down-converted signal and compared this to the input of the filter in the transmitter of the system. They then down sampled the data to detect, synchronize, and extract the DPSK symbols from the waveform and compare them to those in the transmitter. Finally, students demodulated and decoded these DPSK symbols to recover the message bits, which are again compared with those in the transmitter.
After these three features were implemented into the demodulator, students rigorously tested their system by comparing their constellation graph and signal eye diagram to those of the transmitter, which is shown below.
The constellation diagram gives a visual overview of how the different phases in the phase-shift keying modulation scheme matched up to symbols and how they are represented within the signal envelope. They are important because they give a visual overview of how much interference or distortion is in a signal or channel and are a quick way of seeing if everything is functioning normally. The eye diagram gives a similar visual reference in that it helps show all of the different types of symbols within a channel superimposed over each other to see the characteristics of the system. From this students could infer characteristics such as if the symbols were too long, short, or noisy or poorly synchronized. If the eye is “open”, as it is in the above diagram, then it infers minimal distortion in the signal. If the signal was distorted, then the eye pattern begins to close, decreasing the spaces in the pattern.
Four Out of Five Students Would Like to Make More Use of USRPs
After the conclusion of the module on communications system, students completed questionnaires about their satisfaction and provided feedback on the practical session.
More than four out of five students, 82 percent, said that in the future they would like to make use of the USRP in the taught aspects of their course. In addition, 75 percent of students said that they would like to make use of the USRP in their MSc research projects—showing its great potential in all aspects of wireless communications education and research.
One student said that “The USRP gives an avenue for exploration. It is a good tool to bridge the gap between practical and theory.” Whilst another said that “The USRP vividly helps me understand the theory that I learned in class.” This shows that Southampton has created a strong benchmark in practical communications education.
Post Syndicated from National Instruments original https://spectrum.ieee.org/computing/software/how-ai-is-starting-to-influence-wireless-communications
Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities
What happens when artificial intelligence (AI) technology arrives on wireless channels? For a start, AI promises to address the design complexity of radio frequency (RF) systems by employing powerful machine learning algorithms and significantly improving RF parameters such as channel bandwidth, antenna sensitivity and spectrum monitoring.
So far, engineering efforts have been made for smartening individual components in wireless networks via technologies like cognitive radio. However, these piecemeal optimizations targeted at applications such as spectrum monitoring have been labor intensive, and they entail efforts to hand-engineer feature extraction and selection that often take months to design and deploy.
On the other hand, AI manifestations like machine learning and deep learning can invoke data analysis to train radio signal types in a few hours. For instance, a trained deep neural network takes a few milliseconds to perform signal detection and classification as compared to traditional methodologies based on the iterative and algorithmic signal search and signal detection and classification.
It is important to note that such gains also significantly reduce power consumption and computational requirements. Moreover, a learned communication system allows wireless designers to prioritize key design parameters such as throughput, latency, range and power consumption.
More importantly, deep learning-based training models facilitate a better awareness of the operational environment and promise to offer end-to-end learning for creating an optimal radio system. Case in point: a training model that can jointly learn an encoder and decoder for a radio transmitter and receiver while encompassing RF components, antennas and data converters.
Additionally, what technologies like deep learning promise in the wireless realm is the commoditization of the physical layer (PHY) and signal processing design. Combining deep learning-based sensing with active radio waveforms creates a new class of use cases that can intelligently operate in a variety of radio environments.
The following section will present a couple of design case studies that demonstrate the potential of AI technologies in wireless communications.
Two design case studies
First, the OmniSIG software development kit (SDK) from DeepSig Inc. is based on deep learning technology and employs real-time signal processing to allow users to train signal detection and classification sensors.
DeepSig claims that its OmniSIG sensor can detect Wi-Fi, Bluetooth, cellular and other radio signals up to 1,000 times faster than existing wireless technologies. Furthermore, it enables users to understand the spectrum environment and thus facilitate contextual analysis and decision making.
ENSCO, a U.S. government and defense supplier, is training the OmniSIG sensor to detect and classify wireless and radar signals. Here, ENSCO is aiming to deploy AI-based capabilities to overcome the performance limitations of conventionally designed RF systems for signal intelligence.
What DeepSig’s OmniPHY software does is allow users to learn the communication system, and subsequently optimize channel conditions, hostile spectrum environments and hardware performance limitations. The applications include anti-jam capabilities, non-line-of-sight communications, multi-user systems in contested spectrums and mitigation of the effects of hardware distortion.
Another design case study showing how AI technologies like deep learning can impact future hardware architectures and designs is the passive Wi-Fi sensing system for monitoring health, activity and well-being in nursing homes (Figure 2). The continuous surveillance system developed at Coventry University employs gesture recognition libraries and machine learning systems for signal classification and creates a detailed analysis of the Wi-Fi signals that reflect off a patient, revealing patterns of body movements and vital signs.
Residential healthcare systems usually employ wearable devices, camera-based vision systems and ambient sensors, but they entail drawbacks such as physical discomfort, privacy concerns and limited detection accuracy. On the other hand, a passive Wi-Fi sensing system, based on activity recognition and through-wall respiration sensing, is contactless, accurate and minimally invasive.
The passive Wi-Fi sensing for nursing homes has its roots in a research project on passive Wi-Fi radar carried out at University College London. The passive Wi-Fi radar prototype —based on software-defined radio (SDR) solutions from National Instruments (NI) — is completely undetectable and can be used in military and counterterrorism applications.
USRP transceiver plus LabVIEW
A passive Wi-Fi sensing system is a receive-only system that measures the dynamic Wi-Fi signal changes caused by moving indoor objectives across multiple path propagation. Here, AI technologies like machine learning allow engineers to use frequency to measure the phase changing rate during the measurement duration as well as Doppler shift to identify movements.
Machine learning algorithms can establish the link between physical activities and the Doppler-time spectral map associated with gestures such as picking things up or sitting down. The phase of the data batches is accurate enough to discern the small body movements caused by respiration.
Coventry University built a prototype of a passive Wi-Fi sensing system using Universal Software Radio Peripheral (USRP) and LabVIEW software to capture, process and interpret the raw RF signal samples. LabVIEW, an intuitive graphical programming tool for both processors and FPGAs, enables engineers to manage complex system configurations and adjust signal processing parameters to meet the exact requirements.
On the other hand, USRP is an SDR-based tunable transceiver that works in tandem with LabVIEW for prototyping wireless communication systems. It has already been used in prototyping wireless applications such as FM radio, direction finding, RF record and playback, passive radar and GPS simulation.
Engineers at Coventry University have used USRP to capture the raw RF samples and deliver them to the LabVIEW application for speedy signal processing. They have also dynamically changed the data arrays and batch size of analysis routines to adapt the system to slow and fast movements.
Engineers were able to interpret some captured signals and directly link the periodic change of batch phase with gestures and respiration rate. Next, they examined if the phase of the data batches was accurate enough to discern the small body movements caused by respiration.
AI: The next wireless frontier
The above design examples show the potential of AI technologies like machine learning and deep learning to revolutionize the RF design, addressing a broad array of RF design areas and creating new wireless use cases.
These are still the early days of implementing AI in wireless networks. But the availability of commercial products such as USRP suggests that the AI revolution has reached the wireless doorstep.
For more information on the role of AI technologies in wireless communications, go to Ettus Research, which provides SDR platforms like USRP and is a National Instruments’ brand since 2010.
Solve your EMI problems more efficiently with solutions from Rohde & Schwarz.
With this guide, you are now able to discover and analyze EMI in a more systematic and methodical approach to solve your problems.
Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/the-three-advantages-of-assembled-cables
3D cameras already help us make 3D emoji/animoji. Now, ams wants to use 3D sensing to help smartphones capture more accurate colors
Cameras that scan and render objects in 3D are now a standard feature in many smartphones, drones, robots, and automobiles. Paired with the right software, these cameras are making it possible to sense light levels, movements, and textures in more places, and at a lower cost, than was previously possible.
ams (located on the former grounds of an Austrian castle) produces the tiny lasers and low-power light sensors that many of these camera systems rely on to identify hand gestures or track eye movements in an instant. The company’s technology must produce accurate results for a wide variety of consumer and industrial devices that operate in very different environments.
ams has a team of 1,200 engineers, and as demand has grown, ams has focused its R&D resources and budget on designing components for three types of 3D sensing: structured light, time-of-flight, and active stereo vision.
Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/get-tips-to-develop-your-daq-test-systems
Reduce your test development time, increase throughput and improve the accuracy of your test systems
There is a growing trend across all industries to design feature-rich products. You need to thoroughly test your product while meeting market windows and project deadlines. Learn how a data acquisition system could help you achieve all of these goals in this Ebook entitled, Four Things to Consider When Using a DAQ as a Data Logger
Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/webinar/rohde-schwarz-presents-smart-jammer-drfm-testing-test-and-measurement-solutions-for-the-next-level
The webinar introduces the concept of Digital RF Memory Jammers, describes their technology and the respective test and measurement challenges and solutions from Rohde & Schwarz.
The DRFM jammer has become a highly complex key element of the EA suite. It has evolved from a simple repeater with some fading capabilities to a complex electronic attack asset. Some of the more critical tests are verifying proper operation and timing of the deception techniques on the system level, qualifying the individual components, submodules and modules at the RF/IF level, and last but not least making sure that clock jitter and power integrity are addressed early at the design stage. For all these requirements, Rohde & Schwarz offers cutting edge test and measurement solutions. The webinar introduces the concept of Digital RF Memory Jammers, describes their technology and the respective Test and Measurement challenges and solutions from Rohde & Schwarz.
Please note: By downloading a webinar, you’re contact information will be shared with the sponsoring company, Rohde & Schwarz GmbH & Co.KG and the Rohde & Schwarz entity or subsidiary company mentioned in the imprint of www.rohde-schwarz.com, and you may be contacted by them directly via email or phone for marketing or advertising purposes.
Post Syndicated from National Instruments original https://spectrum.ieee.org/computing/it/using-ethernetbased-synchronization-on-the-usrp-n3xx-devices
The USRP N3xx product family supports three different methods of baseband synchronization: external clock and time reference, GPSDO module, and Ethernet-based timing protocol
USRP N3xx Synchronization Options
The USRP N3xx product family supports three different methods of baseband synchronization: external clock and time reference, GPSDO module, and Ethernet-based timing protocol. Using an external clock and time reference source, such as the CDA-2990 accessory, offers a precise and convenient method of baseband synchronization for high channel count systems where devices are located near each other, such as in a rackmount configuration. Using the GPSDO module enables synchronization when the devices are physically separated by large distances such as in small cell, RF sensor, TDOA, and distributed testbed applications. However, the GPSDO method typically has more skew than the other two methods and requires line of sight to satellites. Therefore, indoor, urban, or hostile environments restrict the use of GPSDO. Ethernet-based synchronization enables precise baseband synchronization over large distances in GPS-denied environments. However, this method consumes one of the SFP+ ports of the USRP N3xx devices and therefore reduces the number of connectors available for IQ streaming. This application note provides instructions for synchronizing multiple USRP N3xx devices using the Ethernet-based method.
Ethernet-Based Synchronization Overview
The USRP N3xx product family supports Ethernet-based synchronization using an open source protocol known as White Rabbit. White Rabbit is a fully deterministic Ethernet-based network protocol for general purpose data transfer and synchronization. This project is supported by a collaboration of academic and industry experts such as CERN and GSI Helmholtz Centre for Heavy Ion Research.
White Rabbit is an extension of the IEEE 1588 Precision Time Protocol (PTP) standard, which distributes time references over Ethernet networks. In addition, White Rabbit uses Synchronous Ethernet (SyncE) to distribute a common clock reference over the network across the Ethernet physical layer to ensure frequency syntonization between all nodes. This combination of SyncE and PTP, in addition to further measurements, provides sub-nanosecond synchronization over distances of up to 10 km. The White Rabbit extension of the IEEE 1588-2008 standard is in the final stages of becoming generalized as the IEEE 1588 High Accuracy profile.
The USRP N3xx product family implements the White Rabbit protocol using a combination of the FPGA and dedicated clocking resources. The USRP N3xx operates as a slave node, a White Rabbit master node is required in the network. Seven Solutions provides White Rabbit hardware that works with the USRP N3xx devices to create synchronous clock and time references that are precisely aligned across all devices in the network. See the “Required Accessories” section for details on the required external hardware. The USRP N3xx devices do not support IQ sample streaming over this protocol. Therefore, only one of SFP+ ports is available for streaming when using White Rabbit synchronization.
For more information on the White Rabbit project, visit the links below:
White Rabbit documentation:
Standardization as IEEE1588 High Accuracy:
White Rabbit synchronization utilizes specific optical SFP transceivers and single mode fiber optic cables to achieve precise time alignment, as documented on the project website. The USRP N3xx was tested to work as a White Rabbit slave using the AXGE-1254-0531 SFP transceiver marked in blue, the AXGE-3454-0531 SFP transceiver marked in purple, and a G652 type single mode fiber optic cable.
Seven Solutions is a provider of White Rabbit equipment, including the WR-LEN and the White Rabbit Switch (WRS). The USRP N3xx was tested to work with both the WR-LEN and the WRS products. All accessories required for White Rabbit operation can be purchased directly from the Seven Solutions website. The AXGE SFP transceivers and fiber optic cables are only listed on the website as part of the “KIT WR-LEN” product, but they can also be purchased individually by contacting Seven Solutions.
For more information on White Rabbit accessories, visit the links below:
White Rabbit SFP wiki:
Seven Solutions WR-LEN:
Seven Solutions KIT WR-LEN:
Seven Solutions WRS:
The White Rabbit feature of the USRP N3xx product family is based on standard networking technology, therefore many system topologies are possible. However, the USRP N3xx device only works as a downstream slave node and must receive its synchronization reference from an upstream master node. This section shows examples of typical configurations used to synchronize a network of multiple USRP N3xx devices.
Figure 1 shows a WRS operating as the master node connected to several USRP N3xx devices. Note that a master SFP port requires the purple SFP transceiver mentioned in the previous section, and a slave SFP port requires the blue SFP transceiver. The USRP N3xx use the SFP+ 0 port for White Rabbit and SFP+ 1 port for IQ streaming. This port configuration requires the White Rabbit “WX” FPGA bitfile.
Download all FPGA images for the version of the USRP Hardware Driver (UHD) installed on the host PC by running the following command in a terminal:
Load the WX bitfile by running:
uhd_image_loader –args type=n3xx,addr=ni-n3xx-<DEVICE_SERIAL>-fpga-path=”<UHD_INSTALL_DIRECTORY>/share/uhd/images/usrp_n310_fpga_WX.bit
Using the UHD API, configure the USRP application to use “internal” clock source and “sfp0” time source:
The White Rabbit IP running on the FPGA disciplines the internal VCXO of the USRP N3xx to the clock reference from the upstream master node in the network. See the USRP N3xx block diagram for reference.
The WRS/WR-LEN device needs to be configured as a master on the ports connected to the USRP N3xx modules. Users can make this configuration with the WR-GUI application provided by Seven Solutions, or with a serial console connection to the WRS/WR-LEN device. See the WRS/WR-LEN manual for detailed instructions. After White Rabbit lock is achieved, the standard USRP N3xx synchronization process completes and the devices are ready for use.
In addition to operating as a master, the WRS and WR-LEN devices can operate as a grandmaster by receiving clock and time references from an external source. This feature is useful for situations where the entire White Rabbit network needs to be disciplined to GPS or other high accuracy synchronization equipment such as a rubidium source. See the WRS/WR-LEN documentation for more information on grandmaster mode.
This section provides an example measurement of the timing alignment between multiple USRP N3xx devices synchronized using White Rabbit, with varying fiber cable lengths. As shown in Figure 3, a White Rabbit Switch in master mode is connected to one USRP N3xx device using a 5 km spool of fiber, and to another USRP N3xx device using 1 m of fiber. The synchronization performance was measured by probing the exported PPS signal, which is in the sample clock domain on both USRP N3xx devices thereby demonstrating sample clock and timestamp alignment. The time difference between each PPS edge was measured with an oscilloscope at room temperature in a laboratory environment. As shown in Figure 4, the resulting measurement shows about 222 ps of skew between the two USRP N3xx devices, thereby demonstrating the sub-nanosecond synchronization of White Rabbit over long distances.
The frequency accuracy of the internal oscillator of each USRP N3xx slave node is derived from the frequency accuracy of the upstream master node, in a manner similar to disciplining to an external clock reference source connected to the REF IN port. By connecting a high accuracy frequency source such as a rubidium reference to the master White Rabbit device in grandmaster mode, all USRP N3xx devices in the White Rabbit network would inherit this frequency accuracy.
Post Syndicated from National Instruments original https://spectrum.ieee.org/computing/it/ni-announces-test-ue-offering-for-5g-lab-and-field-trials
Release 15 compliant 5G New Radio non-standalone system can help customers deliver 5G commercial wireless to market faster
NI (Nasdaq: NATI), the provider of platform-based systems that help engineers and scientists solve the world’s greatest engineering challenges, today announced a real-time 5G New Radio (NR) test UE offering. The NI Test UE offering features a fully 3GPP Release 15 non-standalone (NSA) compliant system capable of emulating the full operation of end-user devices or user equipment (UE).
With the 5G commercial rollout this year, engineers must validate the design and functionality of 5G NR infrastructure equipment before productization and release. Based on the rugged PXI Express platform, the NI Test UE offering helps customers test prototypes in the lab and in the field to evaluate them on service operators’ networks. In addition, customers can perform InterOperability Device Testing (IoDT), which is a critical part of the commercialization process to ensure that network equipment works with UE from any vendor and vice versa. The NI Test UE offering can also be used to perform benchmark testing to evaluate the full capabilities of commercial and precommercial micro-cell, small-cell and macro-cell 5G NR gNodeB equipment.
Spirent has worked with NI to add 5G NR support to its existing portfolio of products. “As 5G was picking up steam, we looked to find a world-class 5G NR platform that would outperform the market today and continue to do so as the 5G market matures,” said Clarke Ryan, senior director of Product Development at Spirent. “As a leader in SDR-based radios since 2011, NI was the natural choice to ensure we have the best radio with the best testing capabilities to stay ahead of the curve for our customers.”
The NI Test UE offering provides a flexible system for evaluating 5G technology. Customers can use the SDR front ends to select the sub-6 GHz frequency of their choice. The system scales up to one 100 MHz bandwidth component carrier and can be configured for up to 4×2 MIMO to achieve a maximum throughput of 2.3 Gb/s. The 5G NR Release 15 software includes complete protocol stack software that can connect with a 5G gNodeB while providing real-time diagnostic information. Customers can log diagnostic information to a disk for post-test analysis and debugging and can view it on the software front panel for a real-time visualization of the link’s performance.
“The industry is on the cusp of 5G commercial deployments and mobile operators need to ensure that their infrastructure is 5G enabled in a virtualized, programmable, open and cost-efficient way,” said Neeraj Patel, Vice President and General Manager, Software and Services, Radisys. “NI is leveraging our first-to-market 5G Software Suite as the engine for its Test UE offering. Our complete 5G source code solution for UE, gNB and 5GCN represents a disruptive end-to-end enabling technology for customers to build 5G NR solutions. By powering such first to market test applications together with NI and Spirent, we are accelerating 5G commercialization that will change how the world connects.”
Find more information about the NI Test UE offering for 5G NR at ni.com/5g-test-ue.
NI (ni.com) develops high-performance automated test and automated measurement systems to help you solve your engineering challenges now and into the future. Our open, software-defined platform uses modular hardware and an expansive ecosystem to help you turn powerful possibilities into real solutions.
Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/broadband-chokes-for-bias-tee-applications
Broadband chokes for Bias Tee applications: how to successfully apply a DC bias onto an RF line
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Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/modeling-the-lithium-ion-battery