207 research outputs found
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Measuring Electric Charge and Molecular Coverage on Electrode Surface from Transient Induced Molecular Electronic Signal (TIMES).
Charge density and molecular coverage on the surface of electrode play major roles in the science and technology of surface chemistry and biochemical sensing. However, there has been no easy and direct method to characterize these quantities. By extending the method of Transient Induced Molecular Electronic Signal (TIMES) which we have used to measure molecular interactions, we are able to quantify the amount of charge in the double layers at the solution/electrode interface for different buffer strengths, buffer types, and pH values. Most uniquely, such capabilities can be applied to study surface coverage of immobilized molecules. As an example, we have measured the surface coverage for thiol-modified single-strand deoxyribonucleic acid (ssDNA) as anchored probe and 6-Mercapto-1-hexanol (MCH) as blocking agent on the platinum surface. Through these experiments, we demonstrate that TIMES offers a simple and accurate method to quantify surface charge and coverage of molecules on a metal surface, as an enabling tool for studies of surface properties and surface functionalization for biochemical sensing and reactions
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets
Underwater gliders have been widely used in oceanography for a range of
applications. However, unpredictable events like shark strikes or remora
attachments can lead to abnormal glider behavior or even loss of the
instrument. This paper employs an anomaly detection algorithm to assess
operational conditions of underwater gliders in the real-world ocean
environment. Prompt alerts are provided to glider pilots upon detecting any
anomaly, so that they can take control of the glider to prevent further harm.
The detection algorithm is applied to multiple datasets collected in real
glider deployments led by the University of Georgia's Skidaway Institute of
Oceanography (SkIO) and the University of South Florida (USF). In order to
demonstrate the algorithm generality, the experimental evaluation is applied to
four glider deployment datasets, each highlighting various anomalies happening
in different scenes. Specifically, we utilize high resolution datasets only
available post-recovery to perform detailed analysis of the anomaly and compare
it with pilot logs. Additionally, we simulate the online detection based on the
real-time subsets of data transmitted from the glider at the surfacing events.
While the real-time data may not contain as much rich information as the
post-recovery one, the online detection is of great importance as it allows
glider pilots to monitor potential abnormal conditions in real time.Comment: Accepted in IEEE/MTS OCEANS Gulf Coast 202
Learning binary codes for maximum inner product search
Binary coding or hashing techniques are recognized to accomplish efficient near neighbor search, and have thus attracted broad interests in the recent vision and learning studies. However, such studies have rarely been dedicated to Maximum Inner Product Search (MIPS), which plays a critical role in various vision applications. In this paper, we investigate learning binary codes to exclusively handle the MIPS problem. Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting. Specifically, two sets of coding functions are learned such that the inner products between their generated binary codes can reveal the inner products between original data vectors. We also propose an alternative simpler objective which maximizes the correlations between the inner products of the produced binary codes and raw data vectors. In both objectives, the binary codes and coding functions are simultaneously learned without continuous relaxations, which is the key to achieving high-quality binary codes. We evaluate the proposed method, dubbed Asymmetric Inner-product Binary Coding (AIBC), relying on the two objectives on several large-scale image datasets. Both of them are superior to the state-of-the-art binary coding and hashing methods in performing MIPS tasks
Zero-shot learning via discriminative representation extraction
Zero-shot learning (ZSL) aims to recognize classes whose samples did not appear during training. Existing research focuses on mapping deep visual feature to semantic embedding space explicitly or implicitly. However, ZSL improvements led by discriminative feature transformation is not well studied. In this paper, we propose a ZSL framework that maps semantic embeddings to a discriminative representation space, which are learned in two different ways: Kernelized Linear Discriminant Analysis (KLDA) and Central-loss based Network (CLN). KLDA and CLN can both force samples to be intra-class aggregation and inter-class separation. With the learned discriminative representations, we map class embeddings to representation space using Kernelized Ridge Regression (KRR). Our experiments show that both KLDA+KRR and CLN+KRR surpass state-of-art approaches in both recognition and retrieval task
Real-time Autonomous Glider Navigation Software
Underwater gliders are widely utilized for ocean sampling, surveillance, and
other various oceanic applications. In the context of complex ocean
environments, gliders may yield poor navigation performance due to strong ocean
currents, thus requiring substantial human effort during the manual piloting
process. To enhance navigation accuracy, we developed a real-time autonomous
glider navigation software, named GENIoS Python, which generates waypoints
based on flow predictions to assist human piloting. The software is designed to
closely check glider status, provide customizable experiment settings, utilize
lightweight computing resources, offer stably communicate with dockservers,
robustly run for extended operation time, and quantitatively compare flow
estimates, which add to its value as an autonomous tool for underwater glider
navigation.Comment: OCEANS 2023 Limeric
Anomaly Detection of Underwater Gliders Verified by Deployment Data
This paper utilizes an anomaly detection algorithm to check if underwater
gliders are operating normally in the unknown ocean environment. Glider pilots
can be warned of the detected glider anomaly in real time, thus taking over the
glider appropriately and avoiding further damage to the glider. The adopted
algorithm is validated by two valuable sets of data in real glider deployments,
the University of South Florida (USF) glider Stella and the Skidaway Institute
of Oceanography (SkIO) glider Angus.Comment: 10 pages, 16 figures, accepted by the International Symposium on
Underwater Technology (UT23
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