Sensors

New capabilities involving sensors, data collection, and data analysis have enabled innovations in how engineered systems are monitored and maintained. Whereas each new evolution of maintenance philosophies has relied upon the current technological state, this research examines potential future capabilities in the field of prognostics and health management (PHM). PHM algorithms for predicting the estimated time to failure for a system are based on sensor data, physical models, or a combination of both.

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A dataset comprising a total of 21 individuals has been meticulously compiled, with 9 individuals identified as exhibiting Major Depressive Disorder (MDD) based on the outcomes derived from the PHQ-9 Questionnaire. The remaining 12 individuals in the dataset are classified as non-MDD. 

The dataset encompasses diverse sensor data, including temperature measurements, SpO2 readings, pulse rates, and accelerometer data. It is important to note that all data points were collected within a controlled environment, ensuring reliability and consistency throughout the dataset.

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These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings.  Odors of different 12 samples are taken from these six sensors

 

1- Natural Air

 

2- Fresh Onion

 

3- Fresh Garlic

 

4- Black Lemon

 

5- Tomato

 

6- Petrol

 

7- Gasoline

 

8- Coffee 

 

9- Orange

 

10- Colonia Perfume

 

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These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.

 

 

odors of different 10 samples are taken from these six sensors 

1- Natural Air

2- Fresh Onion

3- Fresh Garlic

4- Fresh Lemon

5- Tomato

6- Petrol

7- Gasoline

8- Coffee 1,2

9- Orange

10- Colonia Perfume 

 

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137 Views

Database of the times the device remained in each state (idle, low power mode, transmitting and listening, respectively), number of hops, hop distance (d), transmission rate (_R) and size of the packet sent (_Nb), measured on the Tmote Sky device using an Aloha Puro protocol with RDC implemented in the Contiki operating system.

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Researchers have widely used commercial and alternative measurement systems in impedance-based structural health monitoring (SHM), where a piezoelectric transducer attached to the monitored structure is excited while its electrical impedance is measured. However, the literature lacks sufficient attention to the required number of cycles of the excitation signal, an issue frequently overlooked by researchers.

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As the field of human-computer interaction continues to evolve, there is a growing need for new methods of gesture recognition that can be used in a variety of applications, from gaming and entertainment to healthcare and robotics. While traditional methods of gesture recognition rely on cameras or other optical sensors, these systems can be limited by factors such as lighting conditions and occlusions.

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328 Views

This data is for Fourfold-N step phase shifting method. There are three types of images here and in each type : '1. bmp', '5. bmp', '9. bmp', and '13. bmp' are the first set of data, '2. bmp', '6. bmp', '10. bmp', and '14. bmp' are the second set of data, '3. bmp', '7. bmp', '11. bmp', and '15. bmp' are the third set of data, and '4. bmp', '8. bmp', '12. bmp', and '16. bmp' are the fourth set of data. And 'g1. bmp', 'g2. bmp', 'g3. bmp', and 'g4. bmp' are Gray code images with auxiliary phase unwrapping.

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Manual palpation of organs played a vital role in detecting abnormalities in open surgeries. However, surgeons
have lost this ability with the development of minimally invasive surgeries. This challenge led to the development of artificial sensors for palpating the patient's organs and tissue. The majority of research done is related to improving the measurement of tissue compliance by the development of versatile force sensors for surgical

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775 Views

This dataset includes input dynamics (keystroke, touch, and mouse), affect data (physiological measurements), video, and text data collected from research participants aged 6 and older. The dataset includes data from a diverse set of participants, identifying as Asian, White, Middle Eastern or North African, Black or African American, and Hispanic, Latino, or of Spanish origin). Additionally, participants represent both iOS and Android users.

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