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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 |
IoT-based Prediction Models in the Environmental Context: A Systematic Literature Review
مدلهای پیشبینی مبتنی بر اینترنت اشیا در زمینه محیطی: مروری بر ادبیات سیستماتیک-2022 Undoubtedly, during the last years climate change has alerted the research community of the natural environment sector. Furthermore, the advent of Internet of Things (IoT) paradigm has enhanced the research activity in the environmental field offering low-cost sensors. Moreover, artificial
intelligence and more specifically, statistical and machine learning methodologies have proved their predictive power in many disciplines and various
real-world problems. As a result of the aforementioned, many scientists of
the environmental research field have performed prediction models exploiting
the strength of IoT data. Hence, insightful information could be extracted
from the review of these research works and for this reason, a Systematic
Literature Review (SLR) is introduced in the present manuscript in order to
summarize the recent studies of the field under specific rules and constraints.
From the SLR, 54 primary studies have been extracted during 2017-2021.
The analysis showed that many IoT-based prediction models have been applied the previous years in 10 different environmental issues, presenting in
the majority of the primary studies promising results.
keywords: Natural Environment | Internet of Things | Prediction Models | Systematic Literature Review |
مقاله انگلیسی |
3 |
IoTracker: A probabilistic event tracking approach for data-intensive IoT Smart Applications
IoTracker: یک رویکرد ردیابی رویداد احتمالی برای برنامههای هوشمند اینترنت اشیا با داده های فشرده-2022 Smart Applications for cities, industry, farming and healthcare use Internet of Things (IoT)
approaches to improve the general quality. A dependency on smart applications implies that any
misbehavior may impact our society with varying criticality levels, from simple inconveniences
to life-threatening dangers. One critical challenge in this area is to overcome the side effects
caused by data loss due to failures in software, hardware, and communication systems, which
may also affect data logging systems. Event traceability and auditing may be impaired when an
application makes automated decisions and the operating log is incomplete. In an environment
where many events happen automatically, an audit system must understand, validate, and
find the root causes of eventual failures. This paper presents a probabilistic approach to track
sequences of events even in the face of logging data loss using Bayesian networks. The results of
the performance analysis with three smart application scenarios show that this approach is valid
to track events in the face of incomplete data. Also, scenarios modeled with Bayesian subnets
highlight a decreasing complexity due to this divide and conquer strategy that reduces the
number of elements involved. Consequently, the results improve and also reveal the potential
for further advancement.
Keywords: Smart applications | Event tracker | Probabilistic tracker | Bayesian networks |
مقاله انگلیسی |
4 |
A robust structural vibration recognition system based on computer vision
یک سیستم قوی تشخیص ارتعاش ساختاری بر اساس بینایی کامپیوتری-2022 Vibration-based structural health monitoring (SHM) systems are useful tools for assessing structural safety performance quantitatively. When employing traditional contact sensors, achieving high-resolution spatial measurements for large-scale structures is challenging, and fixed contact sensors may also lose dependability when the lifetime of the host structure is surpassed. Researchers have paid close attention to computer vision because it is noncontact, saves time and effort, is inexpensive, and has high efficiency in giving visual perception. In advanced noncontact measurements, digital cameras can capture the vibration information of structures remotely and swiftly. Thus, this work studies a system for recognizing structural vibration. The system ensures acquiring high-quality structural vibration signals by the following: 1) Establishing a novel image preprocessing, which includes visual partitioning measurement and image enhancement techniques; 2) initial recognition of structural vibration using phase-based optical flow estimation (POFE), which introduces 2-D Gabor wavelets to extract the independent phase information of the image to track the natural texture targets on the surface of the structure; 3) extracting the practical vibration information of the structure using mode decomposition to remove the complex environment of the camera vibration and other noises; 4) employing phase-based motion magnification (PMM) techniques to magnify small vibration signals, and then recognizing the complete information on the vibration time range of the structure. The research results of the laboratory experiments and field testing conducted under three different cases reveal that the system can recognize structural vibration in complicated environments.
keywords: Computer vision | Phase | Motion estimation | Motion magnification | Mode decomposition | Structural vibration |
مقاله انگلیسی |
5 |
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 |
6 |
Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM
رویکرد دیجیتال دوقلو برای بهبود بهره وری انرژی در روشنایی داخلی بر اساس بینایی کامپیوتر و BIM پویا-2022 Intelligent lighting systems and surveillance systems have become an important part of intelligent buildings. However, the current intelligent lighting system generally adopts independent sensor control and
does not perform multi-source heterogeneous data fusion with other digital systems. This paper fully
considers the linkage between the lighting system and the surveillance system and proposes a digital
twin lighting (DTL) system that mainly consists of three parts. Firstly, a visualized operation and maintenance (VO&M) platform for a DTL system was established based on dynamic BIM. Secondly, the environment perception, key-frame similarity judgment, and multi-channel key-frame cut and merge
mechanism were utilized to preprocess the video stream of the surveillance system in real-time.
Lastly, pedestrians detected using YOLOv4 and the ambient brightness perceived by the environment
perception mechanism were transmitted to the cloud database and were continuously read by the
VO&M platform. The intent here was to aid timely adaptive adjustment of the digital twin and realistic
lighting through the internet. The effectiveness of the proposed method was verified by experimenting
with a surveillance video stream for 14 days. The key results of the experiments are as follows: (1) the
accuracy rate of intelligent decision control reached 95.15%; (2) energy consumption and electricity costs
were reduced by approximately 79%; and (3) the hardware cost and energy consumption of detection
equipment and the time and cost of operation and maintenance (O&M) were greatly reduced.
keywords: Computer vision | Digital Twin | Dynamic BIM | Energy-efficient buildings | Intelligent lighting control |
مقاله انگلیسی |
7 |
High-Stability Cryogenic System for Quantum Computing With Compact Packaged Ion Traps
سیستم برودتی با پایداری بالا برای محاسبات کوانتومی با تله های یونی بسته بندی شده فشرده-2022 Cryogenic environments benefit ion trapping experiments by offering lower motional heating
rates, collision energies, and an ultrahigh vacuum (UHV) environment for maintaining long ion chains
for extended periods of time. Mechanical vibrations caused by compressors in closed-cycle cryostats can
introduce relative motion between the ion and the wavefronts of lasers used to manipulate the ions. Here,
we present a novel ion trapping system where a commercial low-vibration closed-cycle cryostat is used
in a custom monolithic enclosure. We measure mechanical vibrations of the sample stage using an optical
interferometer, and observe a root-mean-square relative displacement of 2.4 nm and a peak-to-peak displacement of 17 nm between free-space beams and the trapping location. We packaged a surface ion trap
in a cryopackage assembly that enables easy handling while creating a UHV environment for the ions. The
trap cryopackage contains activated carbon getter material for enhanced sorption pumping near the trapping
location, and source material for ablation loading. Using 171Yb+ as our ion, we estimate the operating
pressure of the trap as a function of package temperature using phase transitions of zig-zag ion chains as a
probe. We measured the radial mode heating rate of a single ion to be 13 quanta/s on average. The Ramsey
coherence measurements yield 330-ms coherence time for counter-propagating Raman carrier transitions
using a 355-nm mode-locked pulse laser, demonstrating the high optical stability.
INDEX TERMS: Optomechanical design | quantum computing | trapped ions. |
مقاله انگلیسی |
8 |
Hybrid Quantum-Classical Neural Network for Cloud-Supported In-Vehicle Cyberattack Detection
شبکه عصبی ترکیبی کوانتومی کلاسیک برای تشخیص حمله سایبری در خودرو با پشتیبانی از ابر-2022 A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously
compared to classical computers. In a cloud-supported cyber−physical system environment, running a machine learning
application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However,
with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted
by the classical NN to a reduced but more informative feature space to be processed by the existing quantum computers.
In this study, we developed a hybrid quantum-classical NN to detect an amplitude shift cyberattack on an in-vehicle
controller area network dataset. We showed that by using the hybrid quantum-classical NN, it is possible to achieve an
attack detection accuracy of 94%, which is higher than a long short-term memory NN (88%) or quantum NN alone (62%).
Index Terms: Sensor applications, clouds | cyberattack | sensor applications | quantum computing | quantum neural network (NN). |
مقاله انگلیسی |
9 |
Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022 Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The
automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has
not been investigated previously. This determination will release workers from heavy workloads. This study
proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer
vision and geometric imaging theory. This novel method contained three integrated modules. The first module
used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to
detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and
mark points using the transformation of the five detected keypoints and several image processing technologies
such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module,
according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using
the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average
relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening
angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some
segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring
instruments, and advanced transformation methods can be applied to further improve detection accuracy. keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging |
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 |