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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 |
Is the Internet of Things a helpful employee? An exploratory study of discourses of Canadian farmers
آیا اینترنت اشیا یک کارمند مفید است؟ بررسی اکتشافی گفتمان های کشاورزان کانادایی-2022 The increasing global population and the growing demand for high-quality products have called
for the modernization of agriculture. “Internet of Things” is one of the technologies that is pre-
dicted to offer many solutions. We conducted a discourse analysis of 19 interviews with farmers in
Ontario, Canada, asking them to describe their experience of working with IoT and related
technologies. One main discourse with two opposing tendencies was identified: farmers recognize
their relationship with IoT and related technology and view technology as a kind of “employee”,
but some tend to emphasize (1) an optimistic view which is discourse of technology is a “Helpful
Employee”; while others tend to emphasize (2) a pessimistic view which is a discourse of tech-
nology is an “Untrustworthy Employee”. We examine these tendencies in the light of the literature
on organizational behavior and identify potential outcomes of these beliefs. The results suggest
that a farmer’s style of approaching technology can be assessed on a similar scale as managers’
view of their employees and provide a framework for further research. keywords: فناوری اینترنت اشیا | کشاورزی | تحلیل گفتمان | سبک استفاده از تکنولوژی | Internet of things technology | Agriculture | Discourse analysis | Style of use of technology |
مقاله انگلیسی |
3 |
Understanding the effect of surfactants on two-phase flow using computer vision
درک اثر سورفکتانت ها بر جریان دو فازی با استفاده از بینایی کامپیوتر-2022 The effect of surfactants on vertical gas-liquid flow is experimentally investigated in a 12.7 mm diameter
tube at conditions relevant to an ammonia-water bubble absorber. The characteristics of two-phase flow
are studied using an air-water mixture, both with and without the addition of 1-octanol as the surfac-
tant. High-speed videography is used to study the flow patterns and quantify interfacial areas and bubble
velocities. Novel computer vision-based methods are used to analyze and quantify these flow parame-
ters. The addition of 1-octanol results in enhancement in interfacial area due to the prevention of bubble
coalescence leading to many small diameter bubbles. Measured values of interfacial area are compared
with predictions from correlations in the literature, and agreement and differences are interpreted and
discussed. The bubble velocity is measured by object tracking using the optical flow method. Surfactants
lead to a decrease in bubble velocity and increase in the residence time. These are surmised to be due
to the shear stresses caused by the non-uniform concentration distribution of surfactant along the bub-
ble surface. Overall, the addition of surfactants can lead to appreciable enhancement in heat and mass
transfer rates due to their effect on interfacial areas and residence times. keywords: سورفکتانت ها | جریان دو فازی | ناحیه رابط | سرعت | تقویت | تجسم جریان | Surfactants | Two-phase flow | Interfacial area | Velocity | Enhancement | Flow visualization |
مقاله انگلیسی |
4 |
Head tremor in cervical dystonia: Quantifying severity with computer vision
لرزش سر در دیستونی دهانه رحم: کمی کردن شدت با دید کامپیوتری-2022 Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective
observation. Technological developments offer alternatives for measuring HT severity that are objective and
amenable to automation.
Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-
based software system) to quantify oscillatory and directional aspects of HT from video recordings during a
clinical examination and to test its convergent validity with clinical rating scales.
Methods: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video
recordings from an examination segment in which participants were instructed to let their head drift to its most
comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used
Spearman’s correlation to measure the agreement between CMOR and clinical ratings.
Results: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis
was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants.
One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t =
12.89, p < 0.0001) HT axes. CMOR’s HT severity measure positively correlated with the HT item on the Toronto
Western Spasmodic Torticollis Rating Scale-2 (Spearman’s rho = 0.54, p < 0.001).
Conclusions: We demonstrate a new objective method to measure HT severity that requires only conventional
video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity
ratings. keywords: لرزش سر | ویدیو | بینایی کامپیوتر | درجه بندی شدت | TWSTRS | Head tremor | Video | Computer vision | Severity rating | TWSTRS |
مقاله انگلیسی |
5 |
AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022 Modern businesses benefit significantly from advances in computer vision technology, one of the
important sectors of artificially intelligent and computer science research. Advanced computer
vision issues like image processing, object recognition, and biometric authentication can benefit
from using deep learning methods. As smart devices and facilities advance rapidly, current net-
works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing
demand. Classification of images, object classification, and facial recognition software are some
of the most difficult computer vision problems that can be solved using deep learning methods. As
a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently
been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to
solve the above challenges by introducing a new methodology named Optimizing Computer
Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al-
gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s
outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper
to recognize pattern recognition and intelligent management systems and provide driven meth-
odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can
summarize the significant areas for future research and potential solutions, including image
enhancement, machine vision, and access control. keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication |
مقاله انگلیسی |
6 |
Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022 Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture,
engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However,
research on KG updates in the industry is scarce, with most current research focusing on text-based KG
updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the
potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex-
plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to
propose a novel framework called Image2Triplets that can extract explicit visual relationships from images
to update the construction activity KG. Comprehensive experiments on the images of architectural dec-
oration processes have been performed to validate the proposed framework. The results and insights will
contribute new knowledge and evidence to human-object interaction detection, KG update and construc-
tion informatics from the theoretical perspective.
© 2022 Elsevier B.V. All rights reserved. keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph |
مقاله انگلیسی |
7 |
A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
بررسی حملات خصمانه در بینایی کامپیوتر: طبقه بندی، تجسم و جهت گیری های آینده-2022 Deep learning has been widely applied in various fields such as computer vision, natural language pro-
cessing, and data mining. Although deep learning has achieved significant success in solving complex
problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, result-
ing in models that fail to perform their tasks properly, which limits the application of deep learning
in security-critical areas. In this paper, we first review some of the classical and latest representative
adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowl-
edge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze
the subject development in this field based on the information of 5923 articles from Scopus. In the
end, possible research directions for the development about adversarial attacks are proposed based on
the trends deduced by keywords detection analysis. All the data used for visualization are available at:
https://github.com/NanyunLengmu/Adversarial- Attack- Visualization . keywords: یادگیری عمیق | حمله خصمانه | حمله جعبه سیاه | حمله به جعبه سفید | نیرومندی | تجزیه و تحلیل تجسم | Deep learning | Adversarial attack | Black-box attack | White-box attack | Robustness | Visualization analysis |
مقاله انگلیسی |
8 |
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 |
9 |
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 |
10 |
Prediction of total volatile basic nitrogen (TVB-N) and 2-thiobarbituric acid (TBA) of smoked chicken thighs using computer vision during storage at 4 °C
پیشبینی کل نیتروژن بازی فرار (TVB-N) و اسید ۲-تیوباربیتوریک (TBA) ران مرغ دودی با استفاده از بینایی رایانه در طول نگهداری در دمای ۴ درجه سانتیگراد-2022 As the traditional indicators of freshness measurement of meat products, TVB-N and TBA have the disadvantage
of time-consuming, labor-intensive and destructive to the sample. The objective of this study was to investigate
the possibility of computer vision techniques to visualize the variation of TVB-N and TBA during the storage of
smoked chicken thighs. In this study, freshness indicators (TVB-N and TBA) and images of smoked chicken thighs
were obtained simultaneously every 3 days during storage at 4 ◦C. Then, the RGB color space was converted to
HSI and L*a*b* color spaces by color conversion algorithm, and the color parameters (RGB, HSI and L*a*b*)
were correlated with TVB-N and TBA, respectively, for establishing multiple regression models. Finally, visu-
alization maps of the spoilage were established by applying the multiple regression model to each pixel in the
image. The results showed that the multiple linear regression models of TBA and TVB-N based on the color
parameters L*, a*, I, S and R were well correlated (R 2 = 0.993 for TBA and R 2 = 0.970 for TVB-N). Distribution
maps of TBA and TVB-N changed color gradually from blue to red during storage, respectively. In conclusion, this
study demonstrated that distribution maps can be employed as a rapid, objective, and non-destructive method to
predict the TBA and TVB-N values of smoked chicken thighs during storage. keywords: ران مرغ دودی | بینایی کامپیوتر | خنکی | TVB-N | TBA | Smoked chicken thigh | Computer vision | Freshness |
مقاله انگلیسی |