1,625 research outputs found
CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
Human electroencephalography (EEG) is a brain monitoring modality that senses
cortical neuroelectrophysiological activity in high-temporal resolution. One of
the greatest challenges posed in applications of EEG is the unstable signal
quality susceptible to inevitable artifacts during recordings. To date, most
existing techniques for EEG artifact removal and reconstruction are applicable
to offline analysis solely, or require individualized training data to
facilitate online reconstruction. We have proposed CLEEGN, a novel
convolutional neural network for plug-and-play automatic EEG reconstruction.
CLEEGN is based on a subject-independent pre-trained model using existing data
and can operate on a new user without any further calibration. The performance
of CLEEGN was validated using multiple evaluations including waveform
observation, reconstruction error assessment, and decoding accuracy on
well-studied labeled datasets. The results of simulated online validation
suggest that, even without any calibration, CLEEGN can largely preserve
inherent brain activity and outperforms leading online/offline artifact removal
methods in the decoding accuracy of reconstructed EEG data. In addition,
visualization of model parameters and latent features exhibit the model
behavior and reveal explainable insights related to existing knowledge of
neuroscience. We foresee pervasive applications of CLEEGN in prospective works
of online plug-and-play EEG decoding and analysis
Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports
Multi-object tracking algorithms have made significant advancements due to
the recent developments in object detection. However, most existing methods
primarily focus on tracking pedestrians or vehicles, which exhibit relatively
simple and regular motion patterns. Consequently, there is a scarcity of
algorithms that address the tracking of targets with irregular or non-linear
motion, such as multi-athlete tracking. Furthermore, popular tracking
algorithms often rely on the Kalman filter for object motion modeling, which
fails to track objects when their motion contradicts the linear motion
assumption of the Kalman filter. Due to this reason, we proposed a novel online
and robust multi-object tracking approach, named Iterative Scale-Up
ExpansionIoU and Deep Features for multi-object tracking. Unlike conventional
methods, we abandon the use of the Kalman filter and propose utilizing the
iterative scale-up expansion IoU. This approach achieves superior tracking
performance without requiring additional training data or adopting a more
robust detector, all while maintaining a lower computational cost compared to
other appearance-based methods. Our proposed method demonstrates remarkable
effectiveness in tracking irregular motion objects, achieving a score of 75.3%
in HOTA. It outperforms all state-of-the-art online tracking algorithms on the
SportsMOT dataset, covering various kinds of sport scenarios
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning
Recently, vehicle similarity learning, also called re-identification (ReID),
has attracted significant attention in computer vision. Several algorithms have
been developed and obtained considerable success. However, most existing
methods have unpleasant performance in the hazy scenario due to poor
visibility. Though some strategies are possible to resolve this problem, they
still have room to be improved due to the limited performance in real-world
scenarios and the lack of real-world clear ground truth. Thus, to resolve this
problem, inspired by CycleGAN, we construct a training paradigm called
\textbf{RVSL} which integrates ReID and domain transformation techniques. The
network is trained on semi-supervised fashion and does not require to employ
the ID labels and the corresponding clear ground truths to learn hazy vehicle
ReID mission in the real-world haze scenes. To further constrain the
unsupervised learning process effectively, several losses are developed.
Experimental results on synthetic and real-world datasets indicate that the
proposed method can achieve state-of-the-art performance on hazy vehicle ReID
problems. It is worth mentioning that although the proposed method is trained
without real-world label information, it can achieve competitive performance
compared to existing supervised methods trained on complete label information.Comment: Accepted by ECCV 202
Temperature Swing Adsorption Process for CO2 Capture Using Polyaniline Solid Sorbent
AbstractTo capture carbon dioxide from power plant flue gas which consists of 15% CO2 and 85% N2, with a temperature swing adsorption (TSA) by using polyaniline solid sorbent as the adsorbent, is explored experimentally and theoretically. First, single component adsorption equilibrium data of carbon dioxide on polyaniline solid sorbent is obtained by using Micro-Balance Thermo D-200. Then isotherm curves and the parameters are obtained by numerical method. The adsorption is expressed by the Langmuir-Freundlich isotherm. After accomplishment of isotherm curves, the breakthrough curve experiment is investigated with single adsorption column. The experiments test the change in adsorbed gas concentration at the outlet by adsorbed gas, CO2, and non-adsorbed gas, helium. Finally, this study accentuates the TSA experiments on CO2 purity and recovery by operation variable discussion which includes feed pressure, adsorption temperature and desorption temperature to find optimal operation condition. The results of optimal operation condition are CO2 purity of 47.65% with a 92.46% recovery
Fabrication of multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors by using CF4 plasma treatment
Multianalyte CeO2 biosensors have been demonstrated to detect pH, glucose, and urine concentrations. To enhance the multianalyte sensing capability of these biosensors, CF4 plasma treatment was applied to create nanograin structures on the CeO2 membrane surface and thereby increase the contact surface area. Multiple material analyses indicated that crystallization or grainization caused by the incorporation of flourine atoms during plasma treatment might be related to the formation of the nanograins. Because of the changes in surface morphology and crystalline structures, the multianalyte sensing performance was considerably enhanced. Multianalyte CeO2 nanograin electrolyte–insulator–semiconductor biosensors exhibit potential for use in future biomedical sensing device applications
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Despite recent progress in Multiple Object Tracking (MOT), several obstacles
such as occlusions, similar objects, and complex scenes remain an open
challenge. Meanwhile, a systematic study of the cost-performance tradeoff for
the popular tracking-by-detection paradigm is still lacking. This paper
introduces SMILEtrack, an innovative object tracker that effectively addresses
these challenges by integrating an efficient object detector with a Siamese
network-based Similarity Learning Module (SLM). The technical contributions of
SMILETrack are twofold. First, we propose an SLM that calculates the appearance
similarity between two objects, overcoming the limitations of feature
descriptors in Separate Detection and Embedding (SDE) models. The SLM
incorporates a Patch Self-Attention (PSA) block inspired by the vision
Transformer, which generates reliable features for accurate similarity
matching. Second, we develop a Similarity Matching Cascade (SMC) module with a
novel GATE function for robust object matching across consecutive video frames,
further enhancing MOT performance. Together, these innovations help SMILETrack
achieve an improved trade-off between the cost ({\em e.g.}, running speed) and
performance (e.g., tracking accuracy) over several existing state-of-the-art
benchmarks, including the popular BYTETrack method. SMILETrack outperforms
BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets.
Code is available at https://github.com/pingyang1117/SMILEtrack_Officia
Using data envelopment analysis to support best-value contractor selection
Selecting an appropriate contractor or supplier is essential to the successful implementation of a public procurement project. The Taiwan government frequently applies the best-value (BV) tendering method, a multi-criteria evaluation method, to procure projects. However, the selection process of the winner for a BV-based procurement project is generally subjective and thus is easily accused of corruptions. To develop a systematic method to support contractor selection, this study proposes using the Data Envelopment Analysis (DEA) to facilitate the criteria evaluations for each bidder during the short-listing stage. The evaluation results of using the DEA are a list of potential BV winners who are then suggested to enter into the final selection stage. Based on three case studies related to service procurement projects, this research finds that the DEA is suitable of assessing the relative efficiencies among bidders when the BV approach is applied. Lessons learned here should be helpful in applying the DEA to aid bid evaluations in other supplier selection problems.
First published online: 24 Aug 201
Synthesis and Properties of Biodegradable Segmented Poly-ε-caprolactone
Abstract Block copolymers have been used to tune the chemical and physical properties of degradable materials for tissue engineering. In this study, a series of urethane linkages containing segmented poly-ε-caprolactone (sPCL) with various block lengths and weight ratios were synthesized and characterized. The molecular conformations and characteristics of sPCL were investigated using nuclear magnetic resonance, Fourier transform-infrared spectroscopy, and gel permeation chromatography. The effects of the molar ratio and molecular weight of ε-caprolactone precursors on the mechanical properties were studied. The results show that the tensile strength of sPCL, which is tunable, was 35 MPa, much higher than that of a typical PCL sample (16 MPa). In addition, it was found that increasing the number of urethane linkages improves elongation. In vitro studies confirmed that the change of molecular weight of sPCL was significantly accelerated compared to that of homopolymers. These results suggest that sPCL has potential as a tailorable material for implantable devices
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