Signal Processing
Human activity data based on wearable sensors, such as the Inertial Measurement Unit (IMU), have been widely used in human activity recognition. However, most publicly available datasets only collected data from few body parts and the type of data collected is relatively homogeneous. Activity data from local body parts is challenging for recognizing specific activities or complex activities. Hence, we create a new HAR dataset which is colledted from the project named MPJA HAD: A Multi-Position Joint Angles Dataset for Human Activity Recognition Using Wearable Sensors.
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Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture.
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In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory.
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Electroretinography is a non-invasive electrophysiological method standardized by the International Society for Clinical Electrophysiology of Vision (ISCEV). Electroretinography has been used for the clinical application and standardization of electrophysiological protocols for diagnosing the retina since 1989. Electroretinography become fundamental ophthalmological research method that may assesses the state of the retina. To transfer clinical practice to patients the establishment of standardized protocols is an important step.
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Ultrasonic transit time difference meters are prevelent in industry due to their ease of use and lack of moving parts. But, They often suffer from a cross-sensitivity to the speed of sound in the fluid, which is dependent on the temperature. Other factors like changes in the flow profile or trigger jitter in the signal can also have a large impact on the accuracy to which measurements can be made.
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<p><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; border: none windowtext 1.0pt; mso-border-alt: none windowtext 0in; padding: 0in; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Many neurophysiological measurements are affected by mental state tasks.
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For the data acquisition we used JINS MEME smart glasses -- a device furnished with three-point EOG and six-axis inertial measurement unit (IMU) with an accelerometer and a gyroscope. The sampling frequency of the acquired signals is 100 Hz. The data are transmitted to a computer via Bluetooth or USB and can be exported to CSV file.
Data were acquired under real road conditions from 30 healthy subjects, including twenty experienced drivers and ten students attending a driving school. 16 males and 14 females with average age = 38 +-17 participated in the study.
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