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ردیف | عنوان | نوع |
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1 |
Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022 Contact tracing has been proven an essential practice during pandemic outbreaks and is a
critical non-pharmaceutical intervention to reduce mortality rates. While traditional con-
tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based
systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is
steadily growing in smart city environments. This work presents a contact tracing frame-
work that logs smart space users’ co-existence using IoT devices as reference anchors. The
design is non-intrusive as it relies on passive wireless interactions between each user’s
carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti-
lizing received signal strength indicators (RSSI). The proposed framework can log the iden-
tities for the interacting pair, their estimated distance, and the overlapping time duration.
Also, we propose a machine learning-based infection risk classification method to char-
acterize each interaction that relies on RSSI-based attributes and contact details. Finally,
the proposed contact tracing framework’s performance is evaluated through a real-world
case study of actual wireless interactions between users and IoT devices through Bluetooth
Low Energy advertising. The results demonstrate the system’s capability to accurately cap-
ture contact between mobile users and assess their infection risk provided adequate model
training over time.
© 2021 Elsevier B.V. All rights reserved. keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification |
مقاله انگلیسی |
2 |
Discriminating Quantum States in the Presence of a Deutschian CTC: A Simulation Analysis
حالت های کوانتومی متمایز در حضور CTC Deutschian: یک تحلیل شبیه سازی-2022 In an article published in 2009, Brun et al. proved that in the presence of a “Deutschian”
closed timelike curve, one can map K distinct nonorthogonal states (hereafter, input set) to the standard
orthonormal basis of a K-dimensional state space. To implement this result, the authors proposed a quantum
circuit that includes, among SWAP gates, a fixed set of controlled operators (boxes) and an algorithm for
determining the unitary transformations carried out by such boxes. To our knowledge, what is still missing
to complete the picture is an analysis evaluating the performance of the aforementioned circuit from an
engineering perspective. The objective of this article is, therefore, to address this gap through an in-depth
simulation analysis, which exploits the approach proposed by Brun et al. in 2017. This approach relies on
multiple copies of an input state, multiple iterations of the circuit until a fixed point is (almost) reached. The
performance analysis led us to a number of findings. First, the number of iterations is significantly high even
if the number of states to be discriminated against is small, such as 2 or 3. Second, we envision that such
a number may be shortened as there is plenty of room to improve the unitary transformation acting in the
aforementioned controlled boxes. Third, we also revealed a relationship between the number of iterations
required to get close to the fixed point and the Chernoff limit of the input set used: the higher the Chernoff
bound, the smaller the number of iterations. A comparison, although partial, with another quantum circuit
discriminating the nonorthogonal states, proposed by Nareddula et al. in 2018, is carried out and differences
are highlighted.
INDEX TERMS: Benchmarking and performance characterization | classical simulation of quantum systems. |
مقاله انگلیسی |
3 |
The physical and mechanical properties for flexible biomass particles using computer vision
خواص فیزیکی و مکانیکی ذرات زیست توده انعطاف پذیر با استفاده از بینایی کامپیوتری-2022 The combustion and fluidization behavior of biomass depend on the physical properties (size, morphology, and
density) and mechanical performances (elastic modulus, Poisson’s ratio, tensile strength and failure strain), but
their quantitative models have rarely been focused in previous researchers. Hence, a static image measurement
for particle physical properties is studied. Combining the uniaxial tension and digital image correlation tech-
nology, the dynamic image measurement method for the mechanical properties is proposed. The results indicate
that the average roundness, rectangularity, and sphericity of present biomass particles are 0.2, 0.4, and 0.16,
respectively. The equivalent diameter and density obey the skewed normal distribution. The tensile strength and
failure stress are sensitive to stretching rate, fiber size and orientation. The distribution intervals of elastic
modulus and Poisson’s ratio are 30–600 MPa and 0.25–0.307, respectively. The stress–strain curves obtained
from imaging experiments agree well with the result of finite element method. This study provides the operating
parameters for the numerical simulation of particles in the fluidized bed and combustor. Furthermore, the
computer vision measurement method can be extended to the investigations of fossil fuels. keywords: ذرات زیست توده | مشخصات فیزیکی | اجرای مکانیکی | تست کشش | آزمایش تصویربرداری | بینایی کامپیوتر | Biomass particle | Physical properties | Mechanical performances | Tensile testing | Imaging experiment | Computer vision |
مقاله انگلیسی |
4 |
ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022 Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to
detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive,
manual task. This study proposes a novel approach for automated plumage condition assessment using com-
puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects
hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of
input image characteristics, the method was evaluated using images with and without depth information in
resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of
subjective human annotations, plumage condition predictions were compared to manual assessments of one
observer and to matching annotations of two observers. Among all tested settings, performance metrics based on
matching manual annotations of two observers were equal or better than the ones based on annotations of a
single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of
98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it
was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not
generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a
resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using
lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of
plumage conditions in commercial laying hen farms. keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation |
مقاله انگلیسی |
5 |
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 |
6 |
Hash Function Based on Controlled Alternate Quantum Walks With Memory (September 2021)
عملکرد هش بر اساس راه رفتن کوانتومی جایگزین کنترل شده با حافظه (سپتامبر 2021)-2022 We propose a Quantum inspired Hash Function using controlled alternate quantum walks with
Memory on cycles (QHFM), where the jth message bit decides whether to run quantum walk with one-step
memory or to run quantum walk with two-step memory at the jth time step, and the hash value is calculated
from the resulting probability distribution of the walker. Numerical simulation shows that the proposed hash
function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions
based on quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties,
uniform distribution property, and collision resistance property; and theoretical analysis indicates that the
time and space complexity of the new scheme are not greater than those of its peers. The good performance
of QHFM suggests that quantum walks that differ not only in coin operators but also in memory lengths can
be combined to build good hash functions, which, in turn, enriches the construction of controlled alternate
quantum walks.
INDEX TERMS: Controlled alternate quantum walks (CAQW) | hash function | quantum walks with memory (QWM) | statistical properties | time and space complexity. |
مقاله انگلیسی |
7 |
Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022 Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit
and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper
exploits the combination of image processing techniques and machine learning models (Random Forests) to
assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor-
mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma-
chine learning components, was able to build effective models for either the classification problem (visual quality
level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and
ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on
packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with
respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con-
tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and
can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be
analyzed using traditional tools. keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction |
مقاله انگلیسی |
8 |
Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022 There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant
of the successful delivery of site operations. Although manufacturers provide equipment performance hand-
books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis
of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance
monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment
with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated
image libraries are used to train and test several backbone architectures. Experimental results reveal the pre-
cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel
shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the
ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of
potentials to influence current practice of articulated machinery monitoring in projects. keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures |
مقاله انگلیسی |
9 |
Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022 This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap-
proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are
employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the
properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym-
metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern
(ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is
enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification
directly without the need for texture descriptors. Experimental results with a dataset containing real-world
concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the
Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a
classification accuracy rate of roughly 99%. keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning |
مقاله انگلیسی |
10 |
Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022 Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer
vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-
enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten-
tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To
address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled
MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are
introduced and compared. The distribution of academic research outputs is also examined from the aspects of
waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of
shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is
increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un-
evenly distributed in different sectors such as household, commerce and institution, and construction. Too often,
researchers reported some preliminary studies using simplified environments and artificially collected data.
Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in
industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested
researchers to train and evaluate their CV algorithms. keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning |
مقاله انگلیسی |