Signal Processing
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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The Excel file contains samples of a laboratory-generated noisy voltage signal with a dc component under nonideal sampling.
This test signal is generated in a laboratory for assessing power frequency estimation algorithms.
The first column represents the sample time.
The second column represents the voltage signal samples.
The reference fundamental frequency is 46 Hz.
The nominal voltage amplitude is 10 V.
The actual sampling rate varies in the range of 9.99834~10.01027 kHz.
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Abstract: This paper explains a new interactive power meter achieved by TMS 320 a digital signal processing (DSP) technique that defines equation conditions. Q_x = √ { V_rms^2 ∑_(n=1)^∞〖( Qn Vn)^2 }〗 for the importance n harmonics. The definition of reactive current is useful because by reduces its value, while the maximum PF (power factor) can be obtained for non-genetic systems.
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It is suggested to use zero crossing detectors to build a high-precision power-factor meter. Low pass filters are suggested to stop this error source after the influence of input signal distortion is examined. Based on the measurement of voltage, current, and power factor, this system is also proposed as a new type of power standard meter.
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RRN-ATR-Net is a radar echo data set containing the micro-Doppler signatures (m-Ds) of different aerial targets, with a total sample size of 1200. It is available for researchers interested in this field but having difficulty collecting data. The data set is acquired using Texas Instruments' AWR1642BOOST radar sensor and the DCA1000EVM high-speed data acquisition card. The target types subject to acquisition include quadrotor (Phantom3s), fixed-wing (Cessna182), helicopter (T-REX450), and bionic bird (Gogo Bird1020).
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They are a combination of 8 finger gestures and 3 wrist gestures. The 8 finger motions include fist, extension of all fingers, extension of thumb finger, extension of index finger, extension of thumb and index finger, extension of little finger, 3-fingers pinch, and 5-fingers pinch . And the 3 wrist motions are wrist extension, wrist flexion and wrist relaxation. 8-channel sEMG and 1-inertial measurement unit (IMU) were recorded by a self-developed device. The sampling frequency of sEMG and inertial signal was 1000Hz.
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A specially designed waist-worn device with accelerometer, gyroscope, and pressure sensor was utilized to collect information about 18 ADLs and 16 fall types. The falls protocol has been performed in our lab to replicate realistic situations that typically affect workers and older people. In contrast to other datasets that are accessible to the public, we included a new task in the falls, syncope, since it has a high mortality rate among the elderly and is linked to falls. As such, we must take it into account and include it in our fall detection system.
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It is Supplementary Table_S1, the extracted scattering centers for the complex target in Fig. 9(a). Paper abstract: An electromagnetic (EM) metasurface is a passive device capable of manipulating the reflected signal of a radar in both spatial and frequency domains. This paper presents a novel analytical design of EM scattering center model guided passive synthetic aperture radar (SAR) deception based on diverse frequency time-modulation. The objective of this design is to disguise real target as an intended deceptive target and conceal its original EM characteristics.
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This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four di erent motor imagery tasks, namely the imag ination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4). Two sessions on di erent days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session.
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Faces and bodies provide critical cues for social interaction and communication. Their structural encoding depends on configural processing, as suggested by the detrimental effect of stimulus inversion for both faces (i.e., face inversion effect - FIE) and bodies (body inversion effect - BIE). An occipito-temporal negative event-related potential (ERP) component peaking around 170 ms after stimulus onset (N170) is consistently elicited by human faces and bodies and it is affected by the inversion of these stimuli.
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