19,687 research outputs found
Self-calibration of neural recording sensors
This paper reports a calibration system for automatically adjusting the bandpass and gain characteristics of programmable E×G sensors. The calibration mechanism of the bandpass characteristic is based on a mixed-signal tuning loop which uses as feedback signal the output of the data converter following the signal conditioning of the E×G sensor. Intended high-pass and low-pass frequency poles of the transfer function are injected into the loop by means of a direct frequency synthesizer followed by a smoothing atenuator.Ministerio de Ciencia e Innovación TEC2012-3363
A self-calibration circuit for a neural spike recording channel
This paper presents a self-calibration circuit for a neural spike recording channel. The proposed design tunes the bandwidth of the signal acquisition Band-Pass Filter (BPF), which suffers from process variations corners. It also performs the adjustment of the Programmable Gain Amplifier (PGA) gain to maximize the input voltage range of the analog-to-digital conversion. The circuit, which consists on a frequency-controlled signal generator and a digital processor, operates in foreground, is completely autonomous and integrable in an estimated area of 0.026mm 2 , with a power consumption around 450nW. The calibration procedure takes less than 250ms to select the configuration whose performance is closest to the required one.Ministerio de Ciencia e Innovación TEC2009-08447Junta de Andalucía TIC-0281
A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS
In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1
Review of sensors for remote patient monitoring
Remote patient monitoring (RPM) of physiological
measurements can provide an efficient method and high
quality care to patients. The physiological signals
measurement is the initial and the most important factor
in RPM. This paper discusses the characteristics of the
most popular sensors, which are used to obtain vital
clinical signals in prevalent RPM systems.
The sensors discussed in this paper are used to measure
ECG, heart sound, pulse rate, oxygen saturation, blood
pressure and respiration rate, which are treated as the
most important vital data in patient monitoring and
medical examination
Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution
In this paper, we describe a strategy for training neural networks for object
detection in range images obtained from one type of LiDAR sensor using labeled
data from a different type of LiDAR sensor. Additionally, an efficient model
for object detection in range images for use in self-driving cars is presented.
Currently, the highest performing algorithms for object detection from LiDAR
measurements are based on neural networks. Training these networks using
supervised learning requires large annotated datasets. Therefore, most research
using neural networks for object detection from LiDAR point clouds is conducted
on a very small number of publicly available datasets. Consequently, only a
small number of sensor types are used. We use an existing annotated dataset to
train a neural network that can be used with a LiDAR sensor that has a lower
resolution than the one used for recording the annotated dataset. This is done
by simulating data from the lower resolution LiDAR sensor based on the higher
resolution dataset. Furthermore, improvements to models that use LiDAR range
images for object detection are presented. The results are validated using both
simulated sensor data and data from an actual lower resolution sensor mounted
to a research vehicle. It is shown that the model can detect objects from
360{\deg} range images in real time
Artificial Neural Network-based error compensation procedure for low-cost encoders
An Artificial Neural Network-based error compensation method is proposed for
improving the accuracy of resolver-based 16-bit encoders by compensating for
their respective systematic error profiles. The error compensation procedure,
for a particular encoder, involves obtaining its error profile by calibrating
it on a precision rotary table, training the neural network by using a part of
this data and then determining the corrected encoder angle by subtracting the
ANN-predicted error from the measured value of the encoder angle. Since it is
not guaranteed that all the resolvers will have exactly similar error profiles
because of the inherent differences in their construction on a micro scale, the
ANN has been trained on one error profile at a time and the corresponding
weight file is then used only for compensating the systematic error of this
particular encoder. The systematic nature of the error profile for each of the
encoders has also been validated by repeated calibration of the encoders over a
period of time and it was found that the error profiles of a particular encoder
recorded at different epochs show near reproducible behavior. The ANN-based
error compensation procedure has been implemented for 4 encoders by training
the ANN with their respective error profiles and the results indicate that the
accuracy of encoders can be improved by nearly an order of magnitude from
quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding
ANN-generated weight files are used for determining the corrected encoder
angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science
and Technology (MST
- …