360 research outputs found
ETC-based control of underactuated AUVs and AUV formations in a 2D plane
This master thesis is aimed at single auv (autonomous underwater vehicle) and auv formation control in two-dimensional horizontal plane. For sake of increasing services life and saving communication resources, event-triggered mechanism is taken into consideration. two coordinate systems are introduced: earth-fixed frame and body-fixed frame. Some motion parameters and force analysis are used in the process of establishing mathematical model. then the related theorems, lemmas and control method commonly used in analyzing control systems are introduced. then, the auv control system is divided into two subsystems with cascade relationship. considering each subsystem separately, a controller is designed that can simultaneously carry out trajectory tracking and point stabilization. considering the service life of actuator equipment, an event-triggered controller was designed, which can reduce the frequency of actuator adjustment, prolong the service life of equipment. finally, combining the idea of light-of-sight method and virtual structure method, the auv formation tracking control problem is solved similarly to single auv. in deep sea conditions, an event- triggered communicating mechanism is designed to reduce the frequency of communication and adapt to limited communication resources, which balances the reliability and economy. matlab simulink is used to simulate the controller designed in the thesis, and confirms the feasibility of the controller
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow
Multiple object tracking (MOT) has been successfully investigated in computer
vision.
However, MOT for the videos captured by unmanned aerial vehicles (UAV) is
still challenging due to small object size, blurred object appearance, and very
large and/or irregular motion in both ground objects and UAV platforms.
In this paper, we propose FOLT to mitigate these problems and reach fast and
accurate MOT in UAV view.
Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and
light-weight optical flow extractor to extract object detection features and
motion features at a minimum cost.
Given the extracted flow, the flow-guided feature augmentation is designed to
augment the object detection feature based on its optical flow, which improves
the detection of small objects.
Then the flow-guided motion prediction is also proposed to predict the
object's position in the next frame, which improves the tracking performance of
objects with very large displacements between adjacent frames.
Finally, the tracker matches the detected objects and predicted objects using
a spatially matching scheme to generate tracks for every object.
Experiments on Visdrone and UAVDT datasets show that our proposed model can
successfully track small objects with large and irregular motion and outperform
existing state-of-the-art methods in UAV-MOT tasks.Comment: Accepted by ACM Multi-Media 202
RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets
Recently, the field of machine learning has undergone a transition from
model-centric to data-centric. The advancements in diverse learning tasks have
been propelled by the accumulation of more extensive datasets, subsequently
facilitating the training of larger models on these datasets. However, these
datasets remain relatively under-explored. To this end, we introduce a
pioneering approach known as RK-core, to empower gaining a deeper understanding
of the intricate hierarchical structure within datasets. Across several
benchmark datasets, we find that samples with low coreness values appear less
representative of their respective categories, and conversely, those with high
coreness values exhibit greater representativeness. Correspondingly, samples
with high coreness values make a more substantial contribution to the
performance in comparison to those with low coreness values. Building upon
this, we further employ RK-core to analyze the hierarchical structure of
samples with different coreset selection methods. Remarkably, we find that a
high-quality coreset should exhibit hierarchical diversity instead of solely
opting for representative samples. The code is available at
https://github.com/yaolu-zjut/Kcore
Dual-mode adaptive-SVD ghost imaging
In this paper, we present a dual-mode adaptive singular value decomposition
ghost imaging (A-SVD GI), which can be easily switched between the modes of
imaging and edge detection. It can adaptively localize the foreground pixels
via a threshold selection method. Then only the foreground region is
illuminated by the singular value decomposition (SVD) - based patterns,
consequently retrieving high-quality images with fewer sampling ratios. By
changing the selecting range of foreground pixels, the A-SVD GI can be switched
to the mode of edge detection to directly reveal the edge of objects, without
needing the original image. We investigate the performance of these two modes
through both numerical simulations and experiments. We also develop a
single-round scheme to halve measurement numbers in experiments, instead of
separately illuminating positive and negative patterns in traditional methods.
The binarized SVD patterns, generated by the spatial dithering method, are
modulated by a digital micromirror device (DMD) to speed up the data
acquisition. This dual-mode A-SVD GI can be applied in various applications,
such as remote sensing or target recognition, and could be further extended for
multi-modality functional imaging/detection
Diverse biological effects of glycosyltransferase genes from Tartary buckwheat
Background: Tartary buckwheat (Fagopyrum tataricum) is an edible cereal crop whose sprouts have been marketed and commercialized for their higher levels of anti-oxidants, including rutin and anthocyanin. UDP-glucose flavonoid glycosyltransferases (UFGTs) play an important role in the biosynthesis of flavonoids in plants. So far, few studies are available on UFGT genes that may play a role in tartary buckwheat flavonoids biosynthesis. Here, we report on the identification and functional characterization of seven UFGTs from tartary buckwheat that are potentially involved in flavonoid biosynthesis (and have varying effects on plant growth and development when overexpressed in Arabidopsis thaliana.)
Results: Phylogenetic analysis indicated that the potential function of the seven FtUFGT proteins, FtUFGT6, FtUFGT7, FtUFGT8, FtUFGT9, FtUFGT15, FtUFGT40, and FtUFGT41, could be divided into three Arabidopsis thaliana functional subgroups that are involved in flavonoid biosynthesis of and anthocyanin accumulation. A significant positive correlation between FtUFGT8 and FtUFGT15 expression and anthocyanin accumulation capacity was observed in the tartary buckwheat seedlings after cold stress. Overexpression in Arabidopsis thaliana showed that FtUFGT8, FtUFGT15, and FtUFGT41 significantly increased the anthocyanin content in transgenic plants. Unexpectedly, overexpression of FtUFGT6, while not leading to enhanced anthocyanin accumulation, significantly enhanced the growth yield of transgenic plants. When wild-type plants have only cotyledons, most of the transgenic plants of FtUFGT6 had grown true leaves. Moreover, the growth speed of the oxFtUFGT6 transgenic plant root was also significantly faster than that of the wild type. At later growth, FtUFGT6 transgenic plants showed larger leaves, earlier twitching times and more tillers than wild type, whereas FtUFGT15 showed opposite results.
Conclusions: Seven FtUFGTs were isolated from tartary buckwheat. FtUFGT8, FtUFGT15, and FtUFGT41 can significantly increase the accumulation of total anthocyanins in transgenic plants. Furthermore, overexpression of FtUFGT6 increased the overall yield of Arabidopsis transgenic plants at all growth stages. However, FtUFGT15 shows the opposite trend at later growth stage and delays the growth speed of plants. These results suggested that the biological function of FtUFGT genes in tartary buckwheat is diverse
How methanotrophs respond to pH: A review of ecophysiology
Varying pH globally affects terrestrial microbial communities and biochemical cycles. Methanotrophs effectively mitigate methane fluxes in terrestrial habitats. Many methanotrophs grow optimally at neutral pH. However, recent discoveries show that methanotrophs grow in strongly acidic and alkaline environments. Here, we summarize the existing knowledge on the ecophysiology of methanotrophs under different pH conditions. The distribution pattern of diverse subgroups is described with respect to their relationship with pH. In addition, their responses to pH stress, consisting of structure–function traits and substrate affinity traits, are reviewed. Furthermore, we propose a putative energy trade-off model aiming at shedding light on the adaptation mechanisms of methanotrophs from a novel perspective. Finally, we take an outlook on methanotrophs' ecophysiology affected by pH, which would offer new insights into the methane cycle and global climate change
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) utilizes edge computing devices to collaboratively
train a shared model while each device can fully control its local data access.
Generally, FL techniques focus on learning model on independent and identically
distributed (iid) dataset and cannot achieve satisfiable performance on non-iid
datasets (e.g. learning a multi-class classifier but each client only has a
single class dataset). Some personalized approaches have been proposed to
mitigate non-iid issues. However, such approaches cannot handle underlying data
distribution shift, namely data distribution skew, which is quite common in
real scenarios (e.g. recommendation systems learn user behaviors which change
over time). In this work, we provide a solution to the challenge by leveraging
smart-contract with federated learning to build optimized, personalized deep
learning models. Specifically, our approach utilizes smart contract to reach
consensus among distributed trainers on the optimal weights of personalized
models. We conduct experiments across multiple models (CNN and MLP) and
multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate
that our personalized learning models can achieve better accuracy and faster
convergence compared to classic federated and personalized learning. Compared
with the model given by baseline FedAvg algorithm, the average accuracy of our
personalized learning models is improved by 2% to 20%, and the convergence rate
is about 2 faster. Moreover, we also illustrate that our approach is
secure against recent attack on distributed learning.Comment: 12 pages, 9 figure
Quantitative and dark field ghost imaging with ultraviolet light
Ultraviolet (UV) imaging enables a diverse array of applications, such as
material composition analysis, biological fluorescence imaging, and detecting
defects in semiconductor manufacturing. However, scientific-grade UV cameras
with high quantum efficiency are expensive and include a complex thermoelectric
cooling system. Here, we demonstrate a UV computational ghost imaging (UV-CGI)
method to provide a cost-effective UV imaging and detection strategy. By
applying spatial-temporal illumination patterns and using a 325 nm laser
source, a single-pixel detector is enough to reconstruct the images of objects.
To demonstrate its capability for quantitative detection, we use UV-CGI to
distinguish four UV-sensitive sunscreen areas with different densities on a
sample. Furthermore, we demonstrate dark field UV-CGI in both transmission and
reflection schemes. By only collecting the scattered light from objects, we can
detect the edges of pure phase objects and small scratches on a compact disc.
Our results showcase a feasible low-cost solution for non-destructive UV
imaging and detection. By combining it with other imaging techniques, such as
hyperspectral imaging or time-resolved imaging, a compact and versatile UV
computational imaging platform may be realized for future applications.Comment: 9 pages, 5 figure
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