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ردیف | عنوان | نوع |
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1 |
Intelligent context-aware fog node discovery
کشف گره مه آگاه از زمینه هوشمند-2022 Fog computing has been proposed as a mechanism to address certain issues in
cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network
near the edge devices, which we called fog consumers. This decreases latency,
network bandwidth, and response time. Discovering the most relevant fog node,
the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed
Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner.
The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In
this study, the KNN, K-d tree, and brute force algorithms are used to discover
fog nodes based on the context-aware criteria of fog nodes and fog consumers.
The proposed framework is simulated using OMNET++, and the performance of
the proposed algorithms is compared based on performance metrics and execution
time. The accuracy and execution time are the major points of consideration in
the selection of an optimal fog search algorithm. The experiment results show
that the KNN and K-d tree algorithms achieve the same accuracy results of 95 %.
However, the K-d tree method takes less time to find the nearest fog nodes than
KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm
in the IDFD to discover the nearest fog nodes very efficiently and quickly.
keywords: Fog node | Discovery | Context-aware | Intelligent | Fog node discovery |
مقاله انگلیسی |
2 |
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 |
3 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
4 |
Quantum Error Correction at the Threshold: If technologists dont get beyond it, quantum computers will never be big
تصحیح خطای کوانتومی در آستانه: اگر تکنولوژیست ها از آن فراتر نروند، کامپیوترهای کوانتومی هرگز بزرگ نخواهند شد-2022 Dates chIseleD into an
ancient tombstone have more
in common with the data in
your phone or laptop than you may
realize. They both involve conventional,
classical information, carried by hardware that is relatively immune to errors.
The situation inside a quantum computer
is far different: The information itself has
its own idiosyncratic properties, and
compared with standard digital
microelectronics, state-of-the-art
quantum-computer hardware is more
than a billion trillion times as likely to
suffer a fault. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing
from realizing its great promise.
Fortunately, an approach known as
quantum error correction (QEC) can
remedy this problem, at least in principle. A mature body of theory built up
over the past quarter century now provides a solid theoretical foundation, and
experimentalists have demonstrated
dozens of proof-of-principle examples
of QEC. But these experiments still have
not reached the level of quality and
sophistication needed to reduce the
overall error rate in a system.
keywords: |
مقاله انگلیسی |
5 |
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
حل مسئله مسیریابی خودرو با استفاده از الگوریتم بهینه سازی تقریبی کوانتومی-2022 Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having
applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for
optimally routing transportation of goods by vehicles at a given
set of locations. This paper discusses the broader problem of
vehicle traffic management, more popularly known as the Vehicle
Routing Problem (VRP), and investigates the possible use of
near-term quantum devices for solving it. For this purpose,
we give the Ising formulation for VRP and some of its constrained
variants. Then, we present a detailed procedure to solve VRP
by minimizing its corresponding Ising Hamiltonian using a
hybrid quantum-classical heuristic called Quantum Approximate
Optimization Algorithm (QAOA), implemented on the IBM
Qiskit platform. We compare the performance of QAOA with
classical solvers such as CPLEX on problem instances of up to
15 qubits. We find that performance of QAOA has a multifaceted
dependence on the classical optimization routine used, the depth
of the ansatz parameterized by p, initialization of variational
parameters, and problem instance itself.
Index Terms— Vehicle routing problem | ising model | combinatorial optimization | quantum approximate algorithms | variational quantum algorithms. |
مقاله انگلیسی |
6 |
MagLoc : A magnetic induction based localization scheme for fresh food logistics
MagLoc: یک طرح محلی سازی مبتنی بر القای مغناطیسی برای تدارکات مواد غذایی تازه-2022 An IoT infrastructure to continuously monitor the fresh food supply chain can quickly detect
food quality and contamination issues and thereby reduce costs and food wastage. This, in turn,
involves several challenges including the development of inexpensive quality/contamination
sensors to be deployed in a fine grain manner in the food boxes, technologies for sensor
level communications, online data management and analytics, and logistics driven by such
analytics. In this paper, we study the issues related to the communication among sensing
modules deployed in the fresh food boxes and thereby an automated localization of the boxes
that may have quality/contamination issues. In this context we study the near-field magnetic
induction (NFMI) based communication and localization, as the ubiquitous RF communications
suffer high attenuation through the water/mineral rich tissue media. An accurate localization
of the sensors inside boxes within the food pallets is very challenging in this environment. In
this paper we propose a novel magnetic induction based localization scheme, and show that
with a small number of anchor nodes, the localization can be done without any errors for boxes
as small as 0.5 meter on the side, and with small errors even for boxes half as big.
Keywords: Smart sensing | Industrial sensors | Food supply chain | Physical Internet | Magnetic communication | Localization |
مقاله انگلیسی |
7 |
A Secure Anonymous D2D Mutual Authentication and Key Agreement Protocol for IoT
پروتکل ایمن تأیید هویت متقابل D2D و قرارداد کلیدی برای اینترنت اشیا-2022 Internet of Things (IoT) is a developing technology in our time that is prone to security problems
as it uses wireless and shared networks. A challenging scenario in IoT environments is Device-to-
Device (D2D) communication where an authentication server, as a trusted third-party, does not
participate in the Authentication and Key Agreement (AKA) process and only cooperates in the
process of allocating and updating long-term secret keys. Various authentication protocols have
been suggested for such situations but have not been able to meet security and efficiency re-
quirements. This paper examined three related protocols and demonstrated that they failed to
remain anonymous and insecure against Key Compromise Impersonation (KCI) and clogging at-
tacks. To counter these pitfalls, a new D2D mutual AKA protocol that is anonymous, untraceable,
and highly secure was designed that needed no secure channel to generate paired private and
public keys in the registration phase. Formal security proof and security analysis using BAN logic,
Real-Or-Random (ROR) model, and Scyther tool showed that our proposed protocol satisfied
security requirements. The communication and computation costs and energy consumption
comparisons denoted that our design had a better performance than existing protocols. keywords: تأیید اعتبار و توافقنامه کلید (AKA) | ارتباط دستگاه به دستگاه (D2D) | اینترنت اشیا (IoT) | حمله جعل هویت کلیدی (KCI) | Authentication and Key Agreement (AKA) | Device to Device (D2D) communication | Internet of Things (IoT) | Key Compromise Impersonation (KCI) attack |
مقاله انگلیسی |
8 |
Beacon Non-Transmission attack and its detection in intelligent transportation systems
حمله عدم انتقال Beacon و تشخیص آن در سیستم های حمل و نقل هوشمند-2022 Message dropping by intermediate nodes as well as RF jamming attack have been studied
widely in ad hoc networks. In this paper, we thoroughly investigate a new type of attack
in intelligent transportation systems (ITS) defined as Beacon Non-Transmission (BNT) attack
in which attacker is not an intermediate vehicle, but rather a source vehicle. In BNT attack,
a vehicle suppresses the transmissions of its own periodic beacon packets to get rid of the
automated driving misbehavior detection protocols running in ITS, or to mount a Denial-ofService (DoS) attack to cripple the traffic management functionality of ITS. Considering BNT
attack as a critical security threat to ITS, we propose two novel and lightweight techniques to
detect it. Our first technique bases its detection by assuming a certain distribution of the number
of beacons lost from a vehicle while accounting for loss due to channel-error. However, it fails to
classify shortish BNT attacks wherein amount of denial and channel-error loss are comparable.
Our second technique, suitable for identifying both shortish and longish BNT attacks, considers
beacon loss pattern of a vehicle as a time-series data and employs autocorrelation function (ACF)
to determine the existence of an attack. In order to trade-off detection accuracy for equitable
use of limited computational resources, we propose a random inspection model in which the
detection algorithm is executed at random time instances and for randomly selected set of
vehicles. We have performed extensive simulations to evaluate the performance of proposed
detection algorithms under random inspection and a practical attacker model. The results
obtained corroborate the lightweight nature of both techniques, and the efficacy of ACF based
technique over simple threshold based technique in terms of higher detection accuracy as well
as smaller reaction delay.
keywords: DoS attack | Intrusion detection | Driving anomaly detection | Beacon | Autocorrelation function | Intelligent Transportation Systems |
مقاله انگلیسی |
9 |
A Low-Complexity Quantum Principal Component Analysis Algorithm
یک الگوریتم تحلیل مولفه اصلی کوانتومی با پیچیدگی کم-2022 In this article, we propose a low-complexity quantum principal component analysis (qPCA)
algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal
components of the data matrix, rather than all components of the data matrix, to quantum registers, so that the
samples of measurement required can be reduced considerably. Both our qPCA and Lin’s qPCA are based
on quantum singular-value thresholding (QSVT). The key of Lin’s qPCA is to combine QSVT, and modified
QSVT is to obtain the superposition of the principal components. The key of our algorithm, however, is to
modify QSVT by replacing the rotation-controlled operation of QSVT with the controlled-not operation
to obtain the superposition of the principal components. As a result, this small trick makes the circuit
much simpler. Particularly, the proposed qPCA requires three phase estimations, while the state-of-the-art
qPCA requires five phase estimations. Since the runtime of qPCA mainly comes from phase estimations, the
proposed qPCA achieves a runtime of roughly 3/5 of that of the state of the art. We simulate the proposed
qPCA on the IBM quantum computing platform, and the simulation result verifies that the proposed qPCA
yields the expected quantum state.
INDEX TERMS: Quantum computing | quantum principal component analysis (qPCA) | quantum singular value threshold. |
مقاله انگلیسی |
10 |
A Systematic Literature Review of Quantum Computing for Routing Problems
مروری بر ادبیات سیستماتیک محاسبات کوانتومی برای مسائل مسیریابی-2022 Quantum Computing is drawing a significant attention from the current scientific community.
The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production
in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect,
organize and systematically examine the literature published so far on the application of Quantum Computing
to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature
Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research
(from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis
of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been
formulated to give a comprehensive summary of the current state of the art by providing answers related to
the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid),
detected open challenges or most used Quantum Computing device, among others.
INDEX TERMS: Quantum computing | quantum annealer | quantum gates | IBM | DWAVE | traveling salesman problem | vehicle routing problem | routing problems. |
مقاله انگلیسی |