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[articleoftechnology]

What can your microwave tell you about your health?

Many of us don't think of our microwaves and dishwashers when we're looking for health advice, beyond the fact that we should (maybe) cut back on the Hot Pockets and clean up after ourselves.

But, owing to new study from MIT's Computer Science and Artificial Intelligence Laboratory, we may soon be rethinking that (CSAIL). Using only radio signals and a smart electricity meter, the technology, dubbed "Sapple," analyzes in-home appliance usage to better understand human health habits.

The new machine learning model analyses utilization of everyday appliances like microwaves, stoves, and even hair dryers, using data from two in-home sensors, and can pinpoint where and when a given appliance is being used.

Learning appliance usage habits, for example, could help health-care practitioners assess an aging person's ability to conduct various activities of daily life, with the goal of eventually advising on healthy patterns. Personal hygiene, dressing, eating, continence, and movement are examples of these.

"This system relies on passive sensing data and does not need individuals to modify their lifestyles," explains Chen-Yu Hsu, an MIT PhD student who is the primary author of a recent Sapple publication. "It has the potential to improve things like energy savings and efficiency, provide insight into behavioral analytics for smart environments, and give us a better understanding of the everyday activities of seniors living alone."

The "location sensor," which uses radio waves to identify location, has a range of around 40 feet, or enough to cover a standard one-bedroom apartment. To set up, a person can stroll about their residence.

The device, according to the researchers, could be valuable during the Covid-19 pandemic, when there is a growing interest in contactless health and behavior detection. They envision leveraging passive sensor data to free up caregivers' time to visit higher-risk populations and reduce overall in-person contact.

Sapple is part of a growing body of work by the team focused on using wireless sensing to better understand our complex human bodies, including an in-body "GPS" sensor for tracking tumors or dispensing drugs, a wireless smart-home system for monitoring diseases and assisting the elderly in "aging in place," and a system for measuring gait to help monitor and diagnose various ailments.

Previously, energy data from a utility meter was used to learn about appliance usage. However, because the energy data is a mix of numerous appliances' patterns put together, it's difficult to extract out details using this method.

Unsupervised methods, in which the training data isn't labeled, assume that individual appliance patterns are unknown. However, because the utility meter monitors the total energy used by the residence, learning individual appliances and detecting them effectively is quite difficult.

Sapple stays in the unsupervised world, relying on data from a second sensor to help understand appliance usage patterns using self-supervision rather than assuming we know the patterns of particular appliances. The location sensor, for example, catches a person's motion as they approach a microwave, load it with food, and switch it on. After that, the model analyzes the data and determines when specific appliances are switched on.

Sapple could perhaps help decrease our hefty impact on the natural environment, in addition to improving our health. The method might be used to encourage energy-saving practices and enhance forecasting and delivery for utility companies by studying appliance consumption trends within homes.

The team claims that their system's approach eliminates some of the problems that in-home sensors can have. People can be near an appliance without using it, therefore using location data does not always imply appliance usage. Furthermore, many appliances, such as refrigerators, cycle their power and create "background events," and location data from numerous persons in a home may exist, but not all of it is related to appliance usage. Sapple solves these issues by detecting when the two sensor streams get linked and using that information to determine when and where appliances are turned on.

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Scaling Up The Quantum Chip

The integration takes place on a photonic integrated circuit, which is similar to an electronic integrated circuit but instead employs photons to carry information instead of electrons. Photonics provides the basic architecture for efficiently routing and switching photons between circuit components. Instead of the typical silicon used in some integrated circuits, the circuit platform is aluminum nitride."The visible spectrum is emitted by the diamond color centers. Traditional silicon, on the other hand, absorbs visible light, so we choose aluminum nitride for our photonics platform because it is transparent in that domain "Lu clarifies. "Aluminum nitride can also enable photonic switches that work at cryogenic temperatures, which is what we use to control our color centers."Marko Lonar, the Tiantsai Lin Professor of Electrical Engineering at Harvard University, who was not involved in the work, adds, "It's extremely intriguing in terms of the technology." "They were able to create reliable emitters in a photonic platform while keeping very nice quantum memories," says the researcher.

Noel H. Wan, Tsung-Ju Lu, Kevin C. Chen, Michael P. Walsh, Matthew E. Trusheim, Lorenzo De Santis, Eric A. Bersin, Isaac B. Harris, Sara L. Mouradian, and Ian R. Christen of MIT are among the other authors on the Nature publication, along with Edward S. Bielejec of Sandia National Laboratories.

Color centers in diamonds, flaws in the carbon lattice of diamonds where nearby carbon atoms are missing, with their vacancies either filled by a different element or left vacant, make up the artificial atoms in the chiplets. The replacement elements in the MIT chiplets are germanium and silicon. Each center acts as an atom-like emitter with the ability to produce spin states.

Researchers at MIT have devised a method for manufacturing and integrating "artificial atoms," which are formed by atomic-scale flaws in microscopically thin diamond slices, with photonic circuitry, resulting in the world's largest quantum chip.

According to Dirk Englund, an associate professor in MIT's Department of Electrical Engineering and Computer Science, the achievement "marks a turning point" in the development of scalable quantum processors. Quantum computers will require millions of quantum processors, and the new research offers a possible technique to scale up processor manufacture, according to him and his colleagues.

Unlike traditional computers, which process and store data using bits that are either 0s or 1s, quantum computers use quantum bits, or qubits, which can represent either 0 or 1 at the same time. This peculiar trait enables quantum computers to conduct numerous calculations at the same time, solving problems that would be impossible for traditional computers to solve.

The qubits in the new gadget are artificial atoms manufactured from diamond imperfections that can be poked with visible light and microwaves to release quantum information-carrying photons. Englund and his colleagues explain their method in Nature today. It involves carefully selecting "quantum micro chiplets" comprising several diamond-based qubits and placing them on an aluminum nitride photonic integrated circuit.

"Manufacturing such artificial qubit systems at volumes similar to integrated electronics has been the ultimate aim in the past 20 years of quantum engineering," Englund says. "Although great progress has been made in this very busy area of research, fabrication and materials problems have limited the number of emitters per photonic system to two to three."

Englund and colleagues used their hybrid technique to create a 128-qubit device, the largest integrated artificial atom-photonics chip to date.

Wan explains that while diamond color centers create good solid-state qubits, "the bottleneck with this platform is actually designing a system and device architecture that can scale to dozens and millions of qubits." "Unwanted contamination can impact critical quantum features like coherence times because artificial atoms are in a solid crystal." Furthermore, changes in the crystal can lead the qubits to differ from one another, making scaling these devices problematic."

Rather of attempting to construct a massive quantum processor solely out of diamond, the researchers used a modular and hybrid method. "We produce these microscopic chiplets of diamond using semiconductor fabrication processes, from which we pick only the highest quality qubit modules," Wan explains. "After that, we piece by piece combine the chiplets into another chip that 'wires' the chiplets together to form a larger device."

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Deployment planning should start post uat sign off

Deployment is regarded as the most important stage of the project. They also determine the project's overall success. Define and agree on release and deployment strategies with customers/stakeholders is the goal of release and deployment planning. Then make sure that each release package contains a collection of linked assets and service components that are interoperable. Ensure that the integrity of a release package and its constituent components is preserved during the transition operations and that the configuration management system records the information appropriately.

Ascertain that all release and deployment packages can be tracked, installed, tested, confirmed, and/or removed or backed out as needed. Assist in the management of change during the release and deployment processes. Keep track of deviations, risks, and difficulties linked to the new or revised service, and take corrective action as needed. Assist clients and users in optimizing their use of the service to support their business activities by ensuring knowledge transfer. Ensure that the deployment schedule has been prepared and that all stakeholders engaged in the deployment have signed off on their availability before the deployment. It is critical to plan the deployment timetable when in the UAT phase and not during the deployment activities. It is recommended that no new requirements or upgrades be incorporated after User Acceptance testing is completed. They may have an impact on the deployment schedule.

We never recommend doing large deployments on Friday; instead, we recommend doing them on Monday so that we have a full week to monitor the new changes in production. Creating users in production, setting up org-wide defaults for standard objects, installing any 3rd party AppExchange apps, Salesforce configurations such as setting default workflow user, lead owner, Case owner, enabling Chatter, Email deliverability, Salesforce to Outlook configurations, enabling multiple currencies, configuring IP ranges for the organization, and so on should all be done prior to the deployment. Creating and publishing communities are also actions to take. Taking a backup of your current data, Ascertain that the code coverage is more than 75%. Make a list of everything that needs to be deployed. Prepare a change management document, disable workflow-triggered email notifications, and so on.

The most critical method is to build change sets that include all of the components that will be used in deployment and then have the change set verified in production. If the deployment operations are adequately planned ahead of time, the team will have enough time to validate all of the change set's components before the actual deployment. Before the actual deployment, all users must be notified that there will be downtime. Users must be informed of the planned downtime as well as any new modifications or additions they can expect when they check in after the deployment.

If the change sets have previously been validated in production as part of the preparation phase, all that is left is to deploy them. Because all of the test classes will be running against the new features in a complicated org, the deployment may take a few hours. Please allow enough time for the deployment to take place. We'd conduct sanity testing in production to confirm that everything is working as expected and in accordance with the user stories supplied throughout the requirements process. The post-deployment sanity checks also aid in determining whether any errors occurred during the deployment.