25 research outputs found
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Department of Biomedical EngineeringIn this study, we introduce the novel image-guided recording system (IGRS) to interpret the dynamics and distribution of neuronal activities in temporal and spatial scale. Our device was designed to integrate the microelectrode array (MEA) and optical coherence tomography (OCT) at the single-body upright microscope, which enables to image the volumetric brain anatomy and measure multi-sites neuronal activities simultaneously. Using IGRS, we established the time-series mapping protocol on the 3D brain structure after intensive image and signal processing. In order to evaluate the performance of IGRS, neuronal activities of hippocampal region in the brain slice were monitored, and corresponding spatial and temporal mapping was intuitively visualized. Through continuative experiment, it was found that our tool could successfully provide the comprehensive information regarding to excitable neuronal
signals.
In the aspect of stimulation research, various optical methods have been introduced recently as an alternative technique because of spatial resolution, multiple stimulation as well as non-invasive manner. Among optical stimulation methods, single-photon stimulation based on caged glutamate is one of wellknown technique which uses photolysis to release neurotransmitter like a glutamate. Through the preliminary research, we could predict the condition of action potential firing using a photolysis simulation. Based on it, we introduce ball-lensed probe which have several advantages to stimulate neurons. This device is not expensive and can be easily used than commercial light source because it is made of laser module. And it is useful to apply for tissue sample and multi-stimulation because of probe type.
In summary, IGRS and ball-lensed probe would be very efficient tool to investigate and stimulate
neuronal activities and connectivity in various fields.ope
Radar Imaging Based on IEEE 802.11ad Waveform
The extension to millimeter-wave (mmWave) spectrum of communication frequency
band makes it easy to implement a joint radar and communication system using
single hardware. In this paper, we propose radar imaging based on the IEEE
802.11ad waveform for a vehicular setting. The necessary parameters to be
estimated for inverse synthetic aperture radar (ISAR) imaging are sampled
version of round-trip delay, Doppler shift, and vehicular velocity. The delay
is estimated using the correlation property of Golay complementary sequences
embedded on the IEEE 802.11ad preamble. The Doppler shift is first obtained
from least square estimation using radar return signals and refined by
correcting the phase uncertainty of Doppler shift by phase rotation. The
vehicular velocity is determined from the estimated Doppler shifts and an
equation of motion. Finally, an ISAR image is formed with the acquired
parameters. Simulation results show that it is possible to obtain recognizable
ISAR image from a point scatterer model of a realistic vehicular setting.Comment: 6 pages, 6 figures, and accepted for 2020 IEEE Global Communications
Conference (GLOBECOM
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
In this paper, we propose the Interactive Text2Pickup (IT2P) network for
human-robot collaboration which enables an effective interaction with a human
user despite the ambiguity in user's commands. We focus on the task where a
robot is expected to pick up an object instructed by a human, and to interact
with the human when the given instruction is vague. The proposed network
understands the command from the human user and estimates the position of the
desired object first. To handle the inherent ambiguity in human language
commands, a suitable question which can resolve the ambiguity is generated. The
user's answer to the question is combined with the initial command and given
back to the network, resulting in more accurate estimation. The experiment
results show that given unambiguous commands, the proposed method can estimate
the position of the requested object with an accuracy of 98.49% based on our
test dataset. Given ambiguous language commands, we show that the accuracy of
the pick up task increases by 1.94 times after incorporating the information
obtained from the interaction.Comment: 8 pages, 9 figure
Radar Imaging Based on IEEE 802.11ad Waveform in V2I Communications
Since most of vehicular radar systems are already exploiting millimeter-wave
(mmWave) spectra, it would become much more feasible to implement a joint radar
and communication system by extending communication frequencies into the mmWave
band. In this paper, an IEEE 802.11ad waveform-based radar imaging technique is
proposed for vehicular settings. A roadside unit (RSU) transmits the IEEE
802.11ad waveform to a vehicle for communications while the RSU also listens to
the echoes of transmitted waveform to perform inverse synthetic aperture radar
(ISAR) imaging. To obtain high-resolution images of the vehicle, the RSU needs
to accurately estimate round-trip delays, Doppler shifts, and velocity of
vehicle. The proposed ISAR imaging first estimates the round-trip delays using
a good correlation property of Golay complementary sequences in the IEEE
802.11ad preamble. The Doppler shifts are then obtained using least square
estimation from the echo signals and refined to compensate phase wrapping
caused by phase rotation. The velocity of vehicle is determined using an
equation of motion and the estimated Doppler shifts. Simulation results verify
that the proposed technique is able to form high-resolution ISAR images from
point scatterer models of realistic vehicular settings with different
viewpoints. The proposed ISAR imaging technique can be used for various
vehicular applications, e.g., traffic condition analyses or advanced collision
warning systems
Finding the global semantic representation in GAN through Frechet Mean
The ideally disentangled latent space in GAN involves the global
representation of latent space with semantic attribute coordinates. In other
words, considering that this disentangled latent space is a vector space, there
exists the global semantic basis where each basis component describes one
attribute of generated images. In this paper, we propose an unsupervised method
for finding this global semantic basis in the intermediate latent space in
GANs. This semantic basis represents sample-independent meaningful
perturbations that change the same semantic attribute of an image on the entire
latent space. The proposed global basis, called Fr\'echet basis, is derived by
introducing Fr\'echet mean to the local semantic perturbations in a latent
space. Fr\'echet basis is discovered in two stages. First, the global semantic
subspace is discovered by the Fr\'echet mean in the Grassmannian manifold of
the local semantic subspaces. Second, Fr\'echet basis is found by optimizing a
basis of the semantic subspace via the Fr\'echet mean in the Special Orthogonal
Group. Experimental results demonstrate that Fr\'echet basis provides better
semantic factorization and robustness compared to the previous methods.
Moreover, we suggest the basis refinement scheme for the previous methods. The
quantitative experiments show that the refined basis achieves better semantic
factorization while constrained on the same semantic subspace given by the
previous method.Comment: 25 pages, 21 figure
Analyzing the Latent Space of GAN through Local Dimension Estimation
The impressive success of style-based GANs (StyleGANs) in high-fidelity image
synthesis has motivated research to understand the semantic properties of their
latent spaces. In this paper, we approach this problem through a geometric
analysis of latent spaces as a manifold. In particular, we propose a local
dimension estimation algorithm for arbitrary intermediate layers in a
pre-trained GAN model. The estimated local dimension is interpreted as the
number of possible semantic variations from this latent variable. Moreover,
this intrinsic dimension estimation enables unsupervised evaluation of
disentanglement for a latent space. Our proposed metric, called Distortion,
measures an inconsistency of intrinsic tangent space on the learned latent
space. Distortion is purely geometric and does not require any additional
attribute information. Nevertheless, Distortion shows a high correlation with
the global-basis-compatibility and supervised disentanglement score. Our work
is the first step towards selecting the most disentangled latent space among
various latent spaces in a GAN without attribute labels
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection
While LiDAR sensors have been succesfully applied to 3D object detection, the
affordability of radar and camera sensors has led to a growing interest in
fusiong radars and cameras for 3D object detection. However, previous
radar-camera fusion models have not been able to fully utilize radar
information in that initial 3D proposals were generated based on the camera
features only and the instance-level fusion is subsequently conducted. In this
paper, we propose radar-camera multi-level fusion (RCM-Fusion), which fuses
radar and camera modalities at both the feature-level and instance-level to
fully utilize radar information. At the feature-level, we propose a Radar
Guided BEV Encoder which utilizes radar Bird's-Eye-View (BEV) features to
transform image features into precise BEV representations and then adaptively
combines the radar and camera BEV features. At the instance-level, we propose a
Radar Grid Point Refinement module that reduces localization error by
considering the characteristics of the radar point clouds. The experiments
conducted on the public nuScenes dataset demonstrate that our proposed
RCM-Fusion offers 11.8% performance gain in nuScenes detection score (NDS) over
the camera-only baseline model and achieves state-of-the-art performaces among
radar-camera fusion methods in the nuScenes 3D object detection benchmark. Code
will be made publicly available.Comment: 10 pages, 5 figure
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
The discovery of the disentanglement properties of the latent space in GANs
motivated a lot of research to find the semantically meaningful directions on
it. In this paper, we suggest that the disentanglement property is closely
related to the geometry of the latent space. In this regard, we propose an
unsupervised method for finding the semantic-factorizing directions on the
intermediate latent space of GANs based on the local geometry. Intuitively, our
proposed method, called Local Basis, finds the principal variation of the
latent space in the neighborhood of the base latent variable. Experimental
results show that the local principal variation corresponds to the semantic
factorization and traversing along it provides strong robustness to image
traversal. Moreover, we suggest an explanation for the limited success in
finding the global traversal directions in the latent space, especially W-space
of StyleGAN2. We show that W-space is warped globally by comparing the local
geometry, discovered from Local Basis, through the metric on Grassmannian
Manifold. The global warpage implies that the latent space is not well-aligned
globally and therefore the global traversal directions are bound to show
limited success on it.Comment: 23 pages, 19 figure
Lamellar keratoplasty using position-guided surgical needle and M-mode optical coherence tomography
Deep anterior lamellar keratoplasty (DALK) is an emerging surgical technique for the restoration of corneal clarity and vision acuity. The big-bubble technique in DALK surgery is the most essential procedure that includes the air injection through a thin syringe needle to separate the dysfunctional region of the cornea. Even though DALK is a well-known transplant method, it is still challenged to manipulate the needle inside the cornea under the surgical microscope, which varies its surgical yield. Here, we introduce the DALK protocol based on the position-guided needle and M-mode optical coherence tomography (OCT). Depth-resolved 26-gage needle was specially designed, fabricated by the stepwise transitional core fiber, and integrated with the swept source OCT system. Since our device is feasible to provide both the position information inside the cornea as well as air injection, it enables the accurate management of bubble formation during DALK. Our results show that real-time feedback of needle end position was intuitionally visualized and fast enough to adjust the location of the needle. Through our research, we realized that position-guided needle combined with M-mode OCT is a very efficient and promising surgical tool, which also to enhance the accuracy and stability of DALK