Development of Opensource-based Photogrammetric UAV System Using Smart Camera

Normally, aero photography using UAV uses about 430MHz bandwidth RF (radio frequency) modem and navigates and remotely controls through the connection between UAV and ground control system. When using the exhausting method, it has communication range of 1-2km with frequent cross line and since wireless communication sends information using radio wave as a carrier, it has 10mW of signal strength limitation which gave restraints on life my distance communication. The purpose of research is to use communication technologies such as LTE (long-term evolution) of smart camera, Bluetooth, Wi-Fi and other communication modules and cameras that can transfer data to design and develop automatic shooting system that acquires images to UAV at the necessary locations. We conclude that the Photogrammetric UAV system using Smart Camera can not only film images with just one smart camera but also connects UAV system and ground control system together and also able to obtain real-time 3D location information and 3D position information using UAV system, GPS, a gyroscope, an accelerometer, and magnetic measuring sensor which will allow us to use real-time position of the UAV and correction work through non-datum aero triangulation

Time Is MattEr: Temporal Self-supervision for Video Transformers

Understanding temporal dynamics of video is an essential aspect of learning better video representations. Recently, transformer-based architectural designs have been extensively explored for video tasks due to their capability to capture long-term dependency of input sequences. However, we found that these Video Transformers are still biased to learn spatial dynamics rather than temporal ones, and debiasing the spurious correlation is critical for their performance. Based on the observations, we design simple yet effective self-supervised tasks for video models to learn temporal dynamics better. Specifically, for debiasing the spatial bias, our method learns the temporal order of video frames as extra self-supervision and enforces the randomly shuffled frames to have low-confidence outputs. Also, our method learns the temporal flow direction of video tokens among consecutive frames for enhancing the correlation toward temporal dynamics. Under various video action recognition tasks, we demonstrate the effectiveness of our method and its compatibility with state-of-the-art Video Transformers.Comment: Accepted to ICML 2022. Code is available at https://github.com/alinlab/temporal-selfsupervisio

SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video

Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting approximately one billion people world-wide. The current gold standard for diagnosing OSA, Polysomnography (PSG), involves an overnight hospital stay with multiple attached sensors, leading to potential inaccuracies due to the first-night effect. To address this, we present SlAction, a non-intrusive OSA detection system for daily sleep environments using infrared videos. Recognizing that sleep videos exhibit minimal motion, this work investigates the fundamental question: "Are respiratory events adequately reflected in human motions during sleep?" Analyzing the largest sleep video dataset of 5,098 hours, we establish correlations between OSA events and human motions during sleep. Our approach uses a low frame rate (2.5 FPS), a large size (60 seconds) and step (30 seconds) for sliding window analysis to capture slow and long-term motions related to OSA. Furthermore, we utilize a lightweight deep neural network for resource-constrained devices, ensuring all video streams are processed locally without compromising privacy. Evaluations show that SlAction achieves an average F1 score of 87.6% in detecting OSA across various environments. Implementing SlAction on NVIDIA Jetson Nano enables real-time inference (~3 seconds for a 60-second video clip), highlighting its potential for early detection and personalized treatment of OSA.Comment: Accepted to ICCV CVAMD 2023, poste

Identification of TUBB2A by quantitative proteomic analysis as a novel biomarker for the prediction of distant metastatic breast cancer

Background Metastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates. Methods An integrated proteomics approach that combined filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data from FFPE distant metastatic breast cancer tissues. A bioinformatics analysis was performed with regard to gene ontology and signaling pathways using differentially expressed proteins (DEPs) to examine the molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and function of our protein targets. Results A total of 9441 and 8746 proteins were identified from the pooled and individual sample sets, respectively. Based on our criteria, TUBB2A was selected as a novel biomarker candidate. The metastatic activities of TUBB2A were subsequently validated. In our bioinformatics analysis using DEPs, we characterized the overall molecular features of distant metastasis and measured differences in the molecular functions of distant metastatic breast cancer between breast cancer subtypes. Conclusions Our report is the first study to examine the distant metastatic breast cancer proteome using FFPE tissues. The depth of our dataset allowed us to discover a novel biomarker candidate and a proteomic characteristics of distant metastatic breast cancer. Distinct molecular features of various breast cancer subtypes were also established. Our proteomic data constitute a valuable resource for research on distant metastatic breast cancer.This work was supported by the Industrial Strategic Technology Development Program (#10079271 and #20000134), funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea); the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI17C0048); the Basic Science Research Program through the Seoul National University Hospital Research Fund (26-2016-0020); and the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (Grant Number: 2018R1A1A1A05077484)

Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering

To estimate the volume density and color of a 3D point in the multi-view image-based rendering, a common approach is to inspect the consensus existence among the given source image features, which is one of the informative cues for the estimation procedure. To this end, most of the previous methods utilize equally-weighted aggregation features. However, this could make it hard to check the consensus existence when some outliers, which frequently occur by occlusions, are included in the source image feature set. In this paper, we propose a novel source-view-wise feature aggregation method, which facilitates us to find out the consensus in a robust way by leveraging local structures in the feature set. We first calculate the source-view-wise distance distribution for each source feature for the proposed aggregation. After that, the distance distribution is converted to several similarity distributions with the proposed learnable similarity mapping functions. Finally, for each element in the feature set, the aggregation features are extracted by calculating the weighted means and variances, where the weights are derived from the similarity distributions. In experiments, we validate the proposed method on various benchmark datasets, including synthetic and real image scenes. The experimental results demonstrate that incorporating the proposed features improves the performance by a large margin, resulting in the state-of-the-art performance

Neural-Network-Based Automated Synthesis of Transformer Matching Circuits for RF Amplifier Design

Rich experience and intuition play important roles in designing planar transformers (TFs) for contemporary radio frequency integrated circuits (RFICs). In general, RFIC designers have been heavily relying on multiple iterations of full electromagnetic (EM) simulations, which consumes much time and effort. Here, we propose an automated matching circuit synthesizer (AMCS) using neural networks (NNs). The proposed AMCS directly synthesizes a matching circuit combined with a TF throughout the entire design process, ranging from the desired performance to layout. In the AMCS, which is a "spec-to-layout" synthesizer, one NN returns physical parameters of matching circuits, and another NN estimates the electrical performance in two-port S-parameters from the desired impedances. Before the NNs are trained, input feature design is conducted to avoid the one-to-many problem, which cannot be well characterized with an inverse NN. This significantly reduces the time and effort for iterative circuit and EM simulations. The AMCS generated the matching circuit layouts for simple single-stage amplifiers operating at different frequencies up to 70 GHz or in different bandwidths of up to 32.5%. The estimated S-parameters of the amplifiers show good agreement with the EM simulation results.11Nsciescopu

Porous and Conductive Fibrous Carbon for Enhanced Electrocatalytic Oxygen Reduction Reaction in Alkaline Media

Development of an efficient oxygen reduction reaction (ORR) catalyst is a crucial challenge for modern electrochemistry. Herein, a new electrocatalyst, FeAg-CNF (CNF, carbon nanofiber), was fabricated via simple electrospinning and subsequent calcinations as an outstanding ORR electrocatalytic catalyst. FeAg-CNF was subjected to water vapor activation to artificially develop the pore structures and excavate more effective sites. The resultant activated FeAg-CNF (A-FeAg-CNF) exhibits excellent activity and durability, which were better than those of commercial Pt/C. Based on our results, we conclude that the improvement in A-FeAg-CNF is caused by the enhanced pore structure and electrical conductivity. We believe that this study suggests a new insight and is helpful to designing carbon-based ORR catalysts for energy storage and conversion application fields

Reduced formation of peroxide and radical species stabilises iron-based hybrid catalysts in polymer electrolyte membrane fuel cells

International audienc

Comparison of Fucose-Specific Lectins to Improve Quantitative AFP-L3 Assay for Diagnosing Hepatocellular Carcinoma Using Mass Spectrometry

Glycoproteins have many important biological functions. In particular, aberrant glycosylation has been observed in various cancers, such as liver cancer. A well-known glycoprotein biomarker is alpha-fetoprotein (AFP), a surveillance biomarker for hepatocellular carcinoma (HCC) that contains a glycosylation site at asparagine 251. The low diagnostic sensitivity of AFP led researchers to focus on AFP-L3, which has the same sequence as conventional AFP but contains a fucosylated glycan. AFP-L3 has high affinity for Lens culinaris agglutinin (LCA) lectin, prompting many groups to use it for detecting AFP-L3. However, a few studies have identified more effective lectins for fractionating AFP-L3. In this study, we compared the amounts of enriched AFP-L3 with five fucose-specific lectins-LCA, Lotus tetragonolobus lectin (LTL), Ulex europaeus agglutinin I (UEA I), Aleuria aurantia lectin (AAL), and Aspergillus oryzae lectin (AOL)-to identify better lectins and improve HCC diagnostic assays using mass spectrometry (MS). Our results indicate that LTL was the most effective lectin for capturing AFP-L3 species, yielding approximately 3-fold more AFP-L3 than LCA from the same pool of HCC serum samples. Thus, we recommend the use of LTL for AFP-L3 assays, given its potential to improve the diagnostic sensitivity in patients having limited results by conventional LCA assay. The MS data have been deposited to the PeptideAtlas (PASS01752).N