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1 |
EntangleNetSat: A Satellite-Based Entanglement Resupply Network
-2022 In the practical context of quantum networks, quantum teleportation plays a key role in
transmitting quantum information. In the process of teleportation, a maximally entangled pair is consumed.
Through this paper, an efficient scheme of re-establishing entanglement between different nodes in a
quantum network is explored. A hybrid land-satellite network is considered, where the land-based links
are used for short-range communication, and the satellite links are used for transmissions between distant
nodes. This new scheme explores many different possibilities of resupplying the land nodes with entangled
pairs, depending on: the position of the satellites, the number of pairs available and the distance between
the nodes themselves. As to make the entire process as efficient as possible, we consider the situations of
direct transmissions of entangled photons and also the transmissions making use of entanglement swapping.
An analysis is presented for concrete scenarios, sustained by numerical data.
INDEX TERMS: Quantum communication | entanglement | teleportation | entanglement swapping | routing scheme | satellite. |
مقاله انگلیسی |
2 |
A novel method of fish tail fin removal for mass estimation using computer vision
یک روش جدید حذف باله دم ماهی برای تخمین جرم با استفاده از بینایی کامپیوتر-2022 Fish mass estimation is extremely important for farmers to get fish biomass information, which could be useful to
optimize daily feeding and control stocking densities and ultimately determine optimal harvest time. However,
fish tail fin mass does not contribute much to total body mass. Additionally, the tail fin of free-swimming fish is
deformed or bent for most of the time, resulting in feature measurement errors and further affecting mass
prediction accuracy by computer vision. To solve this problem, a novel non-supervised method for fish tail fin
removal was proposed to further develop mass prediction models based on ventral geometrical features without
tail fin. Firstly, fish tail fin was fully automatically removed using the Cartesian coordinate system and image
processing. Secondly, the different features were respectively extracted from fish image with and without tail fin.
Finally, the correlational relationship between fish mass and features was estimated by the Partial Least Square
(PLS). In this paper, tail fins were completely automatically removed and mass estimation model based on area
and area square has been the best tested on the test dataset with a high coefficient of determination (R2) of 0.991,
the root mean square error (RMSE) of 7.10 g, the mean absolute error (MAE) of 5.36 g and the maximum relative
error (MaxRE) of 8.46%. These findings indicated that mass prediction model without fish tail fin can more
accurately estimate fish mass than the model with tail fin, which might be extended to estimate biomass of free-
swimming fish underwater in aquaculture. keywords: برداشتن باله دم | اتوماسیون | ماهی | تخمین انبوه | بینایی کامپیوتر | Tail fin removal | Automation | Fish | Mass estimation | Computer vision |
مقاله انگلیسی |
3 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
4 |
Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision
مناظر معنایی رودخانه: درک و ارزیابی مناظر خطی از تصاویر مایل با استفاده از بینایی کامپیوتری-2022 Traditional approaches for visual perception and evaluation of river landscapes adopt on-site surveys or as-
sessments through photographs. The former is expensive, hindering large-scale analyses, and it is conducted only
on street-level or top-down imagery. The latter only reflects the subjective perception and also entails a laborious
process. Addressing these challenges, this study proposes an alternative: a novel workflow for visual analysis of
urban river landscapes by combining unmanned aerial vehicle (UAV) oblique photography with computer vision
(CV) and virtual reality (VR). The approach is demonstrated with an experiment on a section of the Grand Canal
in China where UAV oblique panoramic imagery has been processed using semantic segmentation for visual
evaluation with an index system we designed. Concurrent surveys, immersive and non-immersive VR, are used to
evaluate these photos, with a total of 111 participants expressing their perceptions across multiple dimensions.
Then, the relationship between the people’s subjective visual perception and the river landscape environment as
seen by computers has been established. The results suggest that using this approach, rivers and surrounding
landscapes can be analyzed automatically and efficiently, and the mean pixel accuracy (MPA) of the developed
model is 90%, which advances state of the art. The results of this study can benefit urban planners in formulating
riverside development policies, analyzing the perception of plans for a future scenario before an area is rede-
veloped, and the method can also aid relevant parties in having a macro understanding of the overall situation of
the river as a basis for follow-up research. Due to simplicity, accuracy and effectiveness, this workflow is
transferable and cost-effective for large-scale investigations of riverscapes and linear heritage. We openly release
Semantic Riverscapes—the dataset we collected and processed, bridging another gap in the field. keywords: ریورساید | باز کردن داده ها | GeoAI | بررسی های هوایی | هواپیماهای بدون سرنشین | واقعیت مجازی | Riverside | Open data | GeoAI | Aerial surveys | Drones | Virtual reality |
مقاله انگلیسی |
5 |
Power to the people: Applying citizen science and computer vision to home mapping for rural energy access
قدرت به مردم: به کارگیری علم شهروندی و بینش رایانه در نقشهبرداری خانه برای دسترسی به انرژی روستایی-2022 To implement effective rural electricity access systems, it is fundamental to identify where potential consumers
live. Here, we test the suitability of citizen science paired with satellite imagery and computer vision to map
remote off-grid homes for electrical system design. A citizen science project called “Power to the People” was
completed on the Zooniverse platform to collect home annotations in Uganda, Kenya, and Sierra Leone. Thou-
sands of citizen scientists created a novel dataset of 578,010 home annotations with an average mapping speed of
7 km2/day. These data were post-processed with clustering to determine high-consensus home annotations. The
raw annotations achieved a recall of 93% and precision of 49%; clustering the annotations increased precision to
69%. These were used to train a Faster R-CNN object detection model, producing detections useful as a first pass
for home-level mapping with a feasible mapping rate of 42,938 km2/day. Detections achieved a precision of 67%
and recall of 36%. This research shows citizen science and computer vision to be a promising pipeline for
accelerated rural home-level mapping to enable energy system design. keywords: دانش شهروندی | بینایی کامپیوتر | دسترسی به برق | نقشه برداری روستایی | تصویربرداری ماهواره ای | سنجش از دور | Citizen science | Computer vision | Electricity access | Rural mapping | Satellite imagery | Remote sensing |
مقاله انگلیسی |
6 |
Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022 Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used
during their storage, transportation purpose. These are responsible factors that decide the freshness of a post
harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the
freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre-
lation has been observed between the colour of the eye and different duration of storage day. It is segmented
from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory
features. These extracted features show a degradation pattern which acts as an indicative parameter to determine
the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The
experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish
freshness. The high accuracy of freshness detection and low computation time makes this non-destructive
methodology efficient for real-world usage in the fish industry and market. keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation |
مقاله انگلیسی |
7 |
Mobile Control Plane Design for Quantum Satellite Backbones
طراحی هواپیمای کنترل سیار برای ستون فقرات ماهواره ای کوانتومی-2022 The interconnection of quantum computers
through the so-called Quantum Internet is a very
promising approach.
The most critical issues concern the physical
layer, considering that the creation of entanglement over long distances is still problematic.
Given the difficulty that usually arises from fiber
optics due to exponential losses, the introduction of intermediate quantum repeaters (QRs)
allows mitigating the problem. A quantum satellite network based on QRs on satellites deployed
over low Earth orbit could make it possible to
overcome the constraints of terrestrial optical
networks. Hence, the recent technological developments in terms of quantum satellite communications motivated our investigation on an ad
hoc quantum satellite backbone design based on
the software defined networking paradigm with a
control plane directly integrated into the constellation itself. Our aim is to outline some guidelines
by comparing several options. Specifically, the
focus is to analyze different architectural solutions
making some considerations on their feasibility,
possible benefits, and costs. Finally, we performed
some simulations on the architectures we considered the most promising, concluding that the integration of the control plane in the constellation
itself is the most appropriate solution.
keywords: |
مقاله انگلیسی |
8 |
The big picture on the internet of things and the smart city: a review of what we know and what we need to know
تصویر بزرگ در اینترنت اشیا و شهر هوشمند: مروری بر آنچه میدانیم و آنچه باید بدانیم-2022 This study examines how the application of the IoT in smart cities is discussed in the current
academic literature. Based on bibliometric techniques, 1,802 articles were retrieved from the
Scopus database and analyzed to identify the temporal nature of IoT research, the most relevant
journals, authors, countries, keywords, and studies. The software tool VOSviewer was used to
build the keyword co-occurrence network and to cluster the pertinent literature. Results show the
significant growth of IoT research in recent years. The most productive authors, journals, and
countries were also identified. The main findings from the keyword co-occurrence clustering and
an in-depth qualitative analysis indicate that the IoT is used alongside other technologies
including cloud computing, big data analytics, blockchain, artificial intelligence, and wireless
telecommunication networks. The major applications of the IoT for smart cities include smart
buildings, transportation, healthcare, smart parking, and smart grids. This review is one of the
first attempts to map global IoT research in a smart city context and uses a comprehensive set of
articles and bibliometric techniques to provide scholars and practitioners with an overview of
what has been studied so far and to identify research gaps at the intersection of the IoT and the
smart city. keywords: اینترنت اشیا | شهر هوشمند | مرور | کتاب سنجی | Internet of things | Smart city | Review | Bibliometrics |
مقاله انگلیسی |
9 |
FANETs in Agriculture - A routing protocol survey
FANETs در کشاورزی - مرور پروتکل مسیریابی-2022 Breakthrough advances on communication technology, electronics and sensors have led to
integrated commercialized products ready to be deployed in several domains. Agriculture
is and has always been a domain that adopts state of the art technologies in time, in order
to optimize productivity, cost, convenience, and environmental protection. The deployment
of Unmanned Aerial Vehicles (UAVs) in agriculture constitutes a recent example. A timely
topic in UAV deployment is the transition from a single UAV system to a multi-UAV system.
Collaboration and coordination of multiple UAVs can build a system that far exceeds the
capabilities of a single UAV. However, one of the most important design problems multi-
UAV systems face is choosing the right routing protocol which is prerequisite for the co-
operation and collaboration among UAVs. In this study, an extensive review of Flying Ad-
hoc network (FANET) routing protocols is performed, where their different strategies and
routing techniques are thoroughly described. A classification of UAV deployment in agri-
culture is conducted resulting in six (6) different applications: Crop Scouting, Crop Survey-
ing and Mapping, Crop Insurance, Cultivation Planning and Management, Application of
Chemicals,and Geofencing. Finally, a theoretical analysis is performed that suggests which
routing protocol can serve better each agriculture application, depending on the mobility
models and the agricultural-specific application requirements.
keywords: کشاورزی هوشمند | کشاورزی دقیق | وسایل نقلیه هوایی بدون سرنشین (UAV) | شبکه های ادوک پرنده (FANET) | پروتکل های مسیریابی | مدل های تحرک | smart farming | precision agriculture | unmanned aerial vehicles (UAVs) | flying adhoc networks (FANETs) | routing protocols | mobility models |
مقاله انگلیسی |
10 |
Evaluation of six commercial SARS-CoV-2 rapid antigen tests in nasopharyngeal swabs: Better knowledge for better patient management?
ارزیابی شش تست آنتی ژن سریع SARS-COV-2 در سواب های نازوفارنکس: دانش بهتر برای مدیریت بهتر بیمار؟-2021 Robust antigen point-of-care SARS-CoV-2 tests have been proposed as an efficient tool to address the COVID-19
pandemic. This requirement was raised after acknowledging the constraints that are brought by molecular
biology. However, worldwide markets have been flooded with cheap and potentially underperforming lateral
flow assays. Herein we retrospectively compared the overall performance of five qualitative rapid antigen SARS-
CoV-2 assays and one quantitative automated test on 239 clinical swabs. While the overall sensitivity and
specificity are relatively similar for all tests, concordance with molecular based methods varies, ranging from
75,7% to 83,3% among evaluated tests. Sensitivity is greatly improved when considering patients with higher
viral excretion (Ct≤33), proving that antigen tests accurately distinguish infectious patients from viral shedding.
These results should be taken into consideration by clinicians involved in patient triage and management, as well
as by national authorities in public health strategies and for mass campaign approaches. keywords: SARS-DONE-2 | تست های آنتی ژن سریع | rt-pcr | کووید -19 | SARS-CoV-2 | Rapid antigen tests | RT-PCR | COVID-19 |
مقاله انگلیسی |