Submission declined on 18 December 2023 by
Mach61 (
talk).
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|
Submission declined on 29 November 2023 by
Timtrent (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources. This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by
Timtrent 7 months ago.
|
Cite review articles, don't write them. This article appears to consist of primary sources, which do not establish notability.
This article contains paid contributions. It may require
cleanup to comply with Wikipedia's
content policies, particularly
neutral point of view. |
Wireless Intelligent Sensing is a form of wireless sensing that uses a systematic framework to introduce digital intelligence into contactless electromagnetic-wave sensor hardware design. [1] The introduction of deep learning into current wireless sensing applications aids in minimizing a sensor's signal-to-noise ratio and improves digital signal processing, [2] converting raw data into practical applications in healthcare, smart home, automotive, and other industries. [3]
The framework of wireless intelligent sensing currently consists of four layers: electromagnetic (EM) wave, signal processing, data analytics, and smart applications. [1]
The EM wave layer includes a radar sensor that transmits and receives EM-wave energy, using different wireless technologies such as Bluetooth, [4] Wi-Fi, [5] UWB, [6] or mmWave for data collection. The design of this EM wave layer is important for accurate data collection, each with their own benefits and challenges, tailored to the application.
The signal processing layer consists of artificial intelligence (AI) algorithms and digital signal processing tailored to specific applications of the technology and the type of wireless technology used. [1] Methods of signal processing may include signal transformation and digital filtering.
The data analytics layer consists of analytic algorithms and tools used to analyze and mine data from the signal processing layer. [1] These algorithms can also vary based on the use-case and type of wireless technology employed in the EM wave layer. Analytics may be computed on the edge, or cloud levels, depending on the application.
The top layer of intelligent sensing uses smart applications to dictate how information from the previous layers can meet the requirements of the application. [1] It utilizes specific machine learning algorithms such as k-means clustering for uses requiring real-time reporting, or deep learning algorithms for applications in non-real-time applications.
Wireless intelligent sensing can be used at home or in assisted living homes, for example, to assist individuals aging in place, seniors, individuals with intellectual disabilities, and caregivers. [7] Intelligence in wireless sensing in-home or in assisted living includes:
Wireless sensing of vitals can monitor respiration rate, heart rate, heart rate variability (HRV)), and more, in the remote care, telehealth, and aging-in-place settings. [9] Leveraging intelligence in wireless sensing, raw vitals data may be turned into information including:
Wireless intelligent sensing has impact in accessible real-time and in long-term monitoring, increasingly recognized in advancing the management of chronic diseases at earlier stages. [12]
Wireless intelligent sensing in the automotive industry can be used inside vehicles to improve passenger and driver safety and comfort functions. [13] One example uses mmWave sensing for child presence detection (CPD) for the Euro NCAP safety program. Intelligence in this application means differentiating children from adults, as well as child seat positioning within the vehicle. Other uses of intelligence in the automotive industry includes:
Submission declined on 18 December 2023 by
Mach61 (
talk). This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|
Submission declined on 29 November 2023 by
Timtrent (
talk). This submission is not adequately supported by
reliable sources. Reliable sources are required so that information can be
verified. If you need help with referencing, please see
Referencing for beginners and
Citing sources. This draft's references do not show that the subject
qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: Declined by
Timtrent 7 months ago.
|
Cite review articles, don't write them. This article appears to consist of primary sources, which do not establish notability.
This article contains paid contributions. It may require
cleanup to comply with Wikipedia's
content policies, particularly
neutral point of view. |
Wireless Intelligent Sensing is a form of wireless sensing that uses a systematic framework to introduce digital intelligence into contactless electromagnetic-wave sensor hardware design. [1] The introduction of deep learning into current wireless sensing applications aids in minimizing a sensor's signal-to-noise ratio and improves digital signal processing, [2] converting raw data into practical applications in healthcare, smart home, automotive, and other industries. [3]
The framework of wireless intelligent sensing currently consists of four layers: electromagnetic (EM) wave, signal processing, data analytics, and smart applications. [1]
The EM wave layer includes a radar sensor that transmits and receives EM-wave energy, using different wireless technologies such as Bluetooth, [4] Wi-Fi, [5] UWB, [6] or mmWave for data collection. The design of this EM wave layer is important for accurate data collection, each with their own benefits and challenges, tailored to the application.
The signal processing layer consists of artificial intelligence (AI) algorithms and digital signal processing tailored to specific applications of the technology and the type of wireless technology used. [1] Methods of signal processing may include signal transformation and digital filtering.
The data analytics layer consists of analytic algorithms and tools used to analyze and mine data from the signal processing layer. [1] These algorithms can also vary based on the use-case and type of wireless technology employed in the EM wave layer. Analytics may be computed on the edge, or cloud levels, depending on the application.
The top layer of intelligent sensing uses smart applications to dictate how information from the previous layers can meet the requirements of the application. [1] It utilizes specific machine learning algorithms such as k-means clustering for uses requiring real-time reporting, or deep learning algorithms for applications in non-real-time applications.
Wireless intelligent sensing can be used at home or in assisted living homes, for example, to assist individuals aging in place, seniors, individuals with intellectual disabilities, and caregivers. [7] Intelligence in wireless sensing in-home or in assisted living includes:
Wireless sensing of vitals can monitor respiration rate, heart rate, heart rate variability (HRV)), and more, in the remote care, telehealth, and aging-in-place settings. [9] Leveraging intelligence in wireless sensing, raw vitals data may be turned into information including:
Wireless intelligent sensing has impact in accessible real-time and in long-term monitoring, increasingly recognized in advancing the management of chronic diseases at earlier stages. [12]
Wireless intelligent sensing in the automotive industry can be used inside vehicles to improve passenger and driver safety and comfort functions. [13] One example uses mmWave sensing for child presence detection (CPD) for the Euro NCAP safety program. Intelligence in this application means differentiating children from adults, as well as child seat positioning within the vehicle. Other uses of intelligence in the automotive industry includes:
-
in-depth (not just passing mentions about the subject)
-
reliable
-
secondary
-
independent of the subject
Make sure you add references that meet these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.