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1 |
Intelligent authentication of 5G healthcare devices: A survey
احراز هویت هوشمند دستگاه های مراقبت بهداشتی 5G: یک مرور-2022 The dynamic nature of wireless links and the mobility of devices connected to the Internet of
Things (IoT) over fifth-generation (5G) networks (IoT-5G), on the one hand, empowers pervasive
healthcare applications. On the other hand, it allows eavesdroppers and other illegitimate
actors to access secret information. Due to the poor time efficiency and high computational
complexity of conventional cryptographic methods and the heterogeneous technologies used,
it is easy to compromise the authentication of lightweight wearable and healthcare devices.
Therefore, intelligent authentication, which relies on artificial intelligence (AI), and sufficient
network resources are extremely important for securing healthcare devices connected to IoT-
5G. This survey considers intelligent authentication and includes a comprehensive overview of
intelligent authentication mechanisms for securing IoT-5G devices deployed in the healthcare
domain. First, it presents a detailed, thoughtful, and state-of-the-art review of IoT-5G, healthcare
technologies, tools, applications, research trends, challenges, opportunities, and solutions. We
selected 20 technical articles from those surveyed based on their strong overlaps with IoT,
5G, healthcare, device authentication, and AI. Second, IoT-5G device authentication, radiofrequency fingerprinting, and mutual authentication are reviewed, characterized, clustered,
and classified. Third, the review envisions that AI can be used to integrate the attributes
of the physical layer and 5G networks to empower intelligent healthcare devices. Moreover,
methods for developing intelligent authentication models using AI are presented. Finally, the
future outlook and recommendations are introduced for IoT-5G healthcare applications, and
recommendations for further research are presented as well. The remarkable contributions and
relevance of this survey may assist the research community in understanding the research gaps
and the research opportunities relating to the intelligent authentication of IoT-5G healthcare
devices.
keywords: اینترنت اشیا (IoT) | امنیت اینترنت اشیا | احراز هویت دستگاه | هوش مصنوعی | امنیت مراقبت های بهداشتی | شبکه های 5g | InternetofThings(IoT) | InternetofThingssecurity | Deviceauthentication | Artificialintelligence | Healthcaresecurity | 5Gnetworks |
مقاله انگلیسی |
2 |
IoT architecture for continuous long term monitoring: Parkinson’s Disease case study
معماری اینترنت اشیا برای نظارت طولانی مدت مداوم: مطالعه موردی بیماری پارکینسون-2022 In recent years, technological advancements and the strengthening of the Internet of Things
concepts have led to significant improvements in the technology infrastructures for remote
monitoring. This includes telemedicine which is the ensemble of technologies and tools involved
in medical services, from consultations, to diagnosis, prescriptions, treatment and patient
monitoring, all done remotely via an Internet connection.
Developing a telemedicine framework capable of monitoring patients over a continuous long-term monitoring window may encounter various issues related to the battery life of the device or the accuracy of the retrieved data. Moreover, it is crucial to develop an IoT architecture that is adaptable to various scenarios and the ongoing changes of the application scenario under analysis. In this work, we present an IoT architecture for continuous long-term monitoring of patients. Furthermore, as a real scenario case study, we adapt our IoT architecture for Parkinson’s Disease management, building up the PDRMA (Parkinson’s disease remote monitoring architecture). Performance analysis for optimal operation with respect to temperature and daily battery life is conducted. Finally, a multi-parameter app for the continuous monitoring of Parkinson’s patients is presented. keywords: IoT | Telemedicine | Continuous long term monitoring | Parkinson’s disease | e-Health |
مقاله انگلیسی |
3 |
iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022 According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is
projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors
who live alone must have their health state closely monitored to avoid unexpected events (such as
a fall). This study explains the underlying principles, methodology, and research that went into
developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure
system, that we call as iRestroom to assess the frailty of elderly people for them to live a
comfortable, independent, and secure life at home. The proposed restroom idea is based on the
required situations, which are determined by user study, socio-cultural and technological trends,
and user requirements. The iRestroom is designed as a multi-sensory place with interconnected
devices where carriers of older persons can access interactive material and services throughout
their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave
Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a
constantly updated reference for the data originating from numerous sensors and devices installed
in different locations throughout the restroom. A small sample of pilot data was obtained, as well
as pertinent web data. The Institutional Review Board (IRB) has approved all the methods. keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning |
مقاله انگلیسی |
4 |
Evolution of Quantum Computing: Theoretical and Innovation Management Implications for Emerging Quantum Industry
تکامل محاسبات کوانتومی: مفاهیم مدیریت نظری و نوآوری برای صنعت کوانتومی در حال ظهور-2022 Quantum computing is a vital research field in science
and technology. One of the fundamental questions hardly known
is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies
for economic, industrial, and social change. This study confronts
the question here by applying methods of computational scientometrics for publication analyses to explain the structure and
evolution of quantum computing research and technologies over
a 30-year period. Results reveal that the evolution of quantum
computing from 1990 to 2020 has a considerable average increase of
connectivity in the network (growth of degree centrality measure),
a moderate increase of the average influence of nodes on the flow
between nodes (little growth of betweenness centrality measure),
and a little reduction of the easiest access of each node to all other
nodes (closeness centrality measure). This evolutionary dynamics
is due to the increase in size and complexity of the network in
quantum computing research over time. This study also suggests
that the network of quantum computing has a transition from
hardware to software research that supports accelerated evolution
of technological pathways in quantum image processing, quantum
machine learning, and quantum sensors. Theoretical implications
of this study show the morphological evolution of the network in
quantum computing from a symmetric to an asymmetric shape
driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation
management based on R&D investments in new technological directions of quantum computing having a high potential for growth
and impact in science and markets.
Index Terms: Innovation management | quantum algorithms | quantum computing (QC) | quantum network | technological change | technological paradigm | technological trajectories. |
مقاله انگلیسی |
5 |
Fault-Tolerant Coherent H∞ Control for Linear Quantum Systems
کنترل منسجم H∞ مقاوم در برابر خطا برای سیستم های کوانتومی خطی-2022 Robustness and reliability are two key requirements for developing practical quantum control systems.
The purpose of this article is to design a coherent feedback
controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop
quantum system has both fault tolerance and H∞ disturbance attenuation performance. This article first extends
the physical realization conditions from the time-invariant
case to the time-varying case for linear stochastic quantum
systems. By relating the fault-tolerant H∞ control problem
to the dissipation properties and the solutions of Riccati
differential equations, an H∞ controller for the quantum
system is then designed by solving a set of linear matrix inequalities. In particular, an algorithm is employed to introduce additional quantum inputs and to construct the corresponding input matrices to ensure the physical realizability
of the quantum controller. Also, we propose a real application of the developed fault-tolerant control strategy to
quantum optical systems. A linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values due to
the fault processes, can be modeled as a linear Markovian
jumping system. It is demonstrated that a quantum H∞
controller can be designed and implemented using some
basic optical components to achieve the desired control
goal for this class of systems.
Index Terms: Coherent feedback control | fault-tolerant quantum control | H∞ control | linear quantum systems | quantum controller. |
مقاله انگلیسی |
6 |
Disintegration testing augmented by computer Vision technology
آزمایش تجزیه با فناوری Vision کامپیوتری تقویت شده است-2022 Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry.
Disintegration testing is often the first step of commercialization and large-scale production of these dosage
forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia
(USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This infor-
mation is subjective, variable, and prone to human error due to manual or physical data collection methods via
the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been
made to automate the analysis of the disintegration process using digital lens and other imaging technologies.
This would provide a non-invasive method to quantitatively determine disintegration time through computer
algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces
through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to
be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and
distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or
other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze
and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not
only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of
disintegration rates and mechanisms in addition to duration. keywords: از هم پاشیدگی | اشکال خوراکی جامد | تست تجزیه | یادگیری ماشین | شبکه های عصبی | Disintegration | Oral Solid Dosage Forms | Disintegration Test | Machine Learning | Neural Networks |
مقاله انگلیسی |
7 |
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 |
8 |
A systematic review on computer vision-based parking lot management applied on public datasets
مرور سیستماتیک مدیریت پارکینگ مبتنی بر بینایی ماشین اعمال شده بر روی مجموعه داده های عمومی-2022 Computer vision-based parking lot management methods have been extensively researched upon owing to their
flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot
image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically
crafted to test computer vision-based methods for parking lot management approaches and consequently
present a systematic and comprehensive review of existing works that employ such datasets. The literature
review identified relevant gaps that require further research, such as the requirement of dataset-independent
approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have
noticed that several important factors such as the presence of the same cars across consecutive images, have
been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis
of the datasets also revealed that certain features that should be present when developing new benchmarks,
such as the availability of video sequences and images taken in more diverse conditions, including nighttime
and snow, have not been incorporated.
keywords: Parking lot | Dataset | Benchmark | Machine learning | Image processing |
مقاله انگلیسی |
9 |
Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision
Cov-Net: یک روش تشخیصی به کمک رایانه برای تشخیص COVID-19 از تصاویر اشعه ایکس قفسه سینه از طریق بینایی ماشین-2022 In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human
beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this
paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19
from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust
feature learning ability. In particular, a modified residual network with asymmetric convolution and attention
mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated
convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic
and low-level detailed information. Experimental results on two public COVID-19 radiography databases have
demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966
and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms
other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of
Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed
that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios.
Consequently, one can conclude that this work has both practical value in providing reliable reference to the
radiologist and theoretical significance in developing methods to build robust features with strong presentation
ability.
keywords: COVID-19 | Computer aided diagnosis (CAD) | Feature learning | Image recognition | Machine vision |
مقاله انگلیسی |
10 |
A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method
یک رویکرد تشخیص و پیشبینی حریق هوشمند ترکیبی در زمان واقعی از طریق دوربینها بر اساس روش بینایی کامپیوتری-2022 Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very
limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire
circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire
detection and forecasting approach through cameras is discussed with extracting and predicting fire development
characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to charac-
terize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network
(RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire
forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accu-
racies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error
(MRE) of extraction by RCNN for the three parameters are around 4–13%, 6–20% and 11–37%, respectively.
Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4–13%, 11–33% and 12–48%,
respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire devel-
opment and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of
accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics. keywords: ایمنی آتش سوزی صنعتی | تشخیص حریق | پیش بینی آتش سوزی | تجزیه و تحلیل آتش سوزی | هوش مصنوعی | Industrial fire safety | Fire detection | Fire forecasting | Fire analysis | Artificial intelligence |
مقاله انگلیسی |