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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 |
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 |
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 |
Efficient Quantum Network Communication Using Optimized Entanglement Swapping Trees
ارتباطات شبکه کوانتومی کارآمد با استفاده از درختان درهم تنیدگی بهینه-2022 Quantum network communication is challenging, as the no-cloning theorem in the quantum
regime makes many classical techniques inapplicable; in particular, the direct transmission of qubit states
over long distances is infeasible due to unrecoverable errors. For the long-distance communication of
unknown quantum states, the only viable communication approach (assuming local operations and classical
communications) is the teleportation of quantum states, which requires a prior distribution of the entangled
pairs (EPs) of qubits. The establishment of EPs across remote nodes can incur significant latency due to the
low probability of success of the underlying physical processes. The focus of our work is to develop efficient
techniques that minimize EP generation latency. Prior works have focused on selecting entanglement paths;
in contrast, we select entanglement swapping trees—a more accurate representation of the entanglement
generation structure. We develop a dynamic programming algorithm to select an optimal swapping tree for a
single pair of nodes, under the given capacity and fidelity constraints. For the general setting, we develop an
efficient iterative algorithm to compute a set of swapping trees. We present simulation results, which show
that our solutions outperform the prior approaches by an order of magnitude and are viable for long-distance
entanglement generation.
INDEX TERMS: Quantum communications | quantum networks (QNs). |
مقاله انگلیسی |
5 |
Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سختافزار-نرمافزار روی تراشه مبتنی بر شبکههای عصبی عمیق برای کاربرد بینایی ماشین-2022 Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial
intelligence (AI) in vision applications that make these systems intelligent and able to make
decisions close to or similar to humans. In this context, the AI’s integration on embedded
systems poses many challenges, given that its performance depends on data volume and
quality they assimilate to learn and improve. This returns to the energy consumption and
cost constraints of the FPGA-SoC that have limited processing, memory, and communication
capacity. Despite this, the AI algorithm implementation on embedded systems can drastically
reduce energy consumption and processing times, while reducing the costs and risks associated
with data transmission. Therefore, its efficiency and reliability always depend on the designed
prototypes. Within this range, this work proposes two different designs for the Traffic Sign
Recognition (TSR) application based on the convolutional neural network (CNN) model,
followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based
TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s,
there is room for improvement using programmable logic (PL) and processing system (PS) in a
hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers
will be accelerated to provide a hardware accelerator for each layer where direct memory
access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased
hardware cost, and execution time optimization of 2.13 s, but, there was still room for design
optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated
to be a single computation engine where BRAM interface is used. The implementation results
prove that our proposed embedded TSR design achieves the best performances compared to the
first proposed architectures, in terms of execution time of about 0.03 s, computation roof of
about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1 |
مقاله انگلیسی |
6 |
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 |
7 |
EP-PQM: Efficient Parametric Probabilistic Quantum Memory With Fewer Qubits and Gates
EP-PQM: حافظه کوانتومی احتمالی پارامتریک کارآمد با کیوبیت ها و گیت های کمتر-2022 Machine learning (ML) classification tasks can be carried out on a quantum computer (QC)
using probabilistic quantum memory (PQM) and its extension, parametric PQM (P-PQM), by calculating
the Hamming distance between an input pattern and a database of r patterns containing z features with
a distinct attributes. For PQM and P-PQM to correctly compute the Hamming distance, the feature must
be encoded using one-hot encoding, which is memory intensive for multiattribute datasets with a > 2. We
can represent multiattribute data more compactly by replacing one-hot encoding with label encoding; both
encodings yield the same Hamming distance. Implementing this replacement on a classical computer is
trivial. However, replacing these encoding schemes on a QC is not straightforward because PQM and P-PQM
operate at the bit level, rather than at the feature level (a feature is represented by a binary string of 0’s and
1’s). We present an enhanced P-PQM, called efficient P-PQM (EP-PQM), that allows label encoding of data
stored in a PQM data structure and reduces the circuit depth of the data storage and retrieval procedures.
We show implementations for an ideal QC and a noisy intermediate-scale quantum (NISQ) device. Our
complexity analysis shows that the EP-PQM approach requires O(z log2(a)) qubits as opposed to O(za)
qubits for P-PQM. EP-PQM also requires fewer gates, reducing gate count from O(rza) to O(rz log2(a)).
For five datasets, we demonstrate that training an ML classification model using EP-PQM requires 48% to
77% fewer qubits than P-PQM for datasets with a > 2. EP-PQM reduces circuit depth in the range of 60% to
96%, depending on the dataset. The depth decreases further with a decomposed circuit, ranging between 94%
and 99%. EP-PQM requires less space; thus, it can train on and classify larger datasets than previous PQM
implementations on NISQ devices. Furthermore, reducing the number of gates speeds up the classification
and reduces the noise associated with deep quantum circuits. Thus, EP-PQM brings us closer to scalable ML
on an NISQ device.
INDEX TERMS: Efficient encoding | label encoding | quantum memory. |
مقاله انگلیسی |
8 |
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 |
9 |
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 |
10 |
Barriers to computer vision applications in pig production facilities
موانع برنامه های بینایی کامپیوتری در تاسیسات تولید خوک-2022 Surveillance and analysis of behavior can be used to detect and characterize health disruption and welfare status
in animals. The accurate identification of changes in behavior is a time-consuming task for caretakers in large,
commercial pig production systems and requires strong observational skills and a working knowledge of animal
husbandry and livestock systems operations. In recent years, many studies have explored the use of various
technologies and sensors to assist animal caretakers in monitoring animal activity and behavior. Of these
technologies, computer vision offers the most consistent promise as an effective aid in animal care, and yet, a
systematic review of the state of application of this technology indicates that there are many significant barriers
to its widespread adoption and successful utilization in commercial production system settings. One of the most
important of these barriers is the recognition of the sources of errors from objective behavior labeling that are not
measurable by current algorithm performance evaluations. Additionally, there is a significant disconnect between the remarkable advances in computer vision research interests and the integration of advances and
practical needs being instituted by scientific experts working in commercial animal production partnerships. This
lack of synergy between experts in the computer vision and animal health and production sectors means that
existing and emerging datasets tend to have a very particular focus that cannot be easily pivoted or extended for
use in other contexts, resulting in a generality versus particularity conundrum.
This goal of this paper is to help catalogue and consider the major obstacles and impediments to the effective
use of computer vision associated technologies in the swine industry by offering a systematic analysis of computer vision applications specific to commercial pig management by reviewing and summarizing the following:
(i) the purpose and associated challenges of computer vision applications in pig behavior analysis; (ii) the use of
computer vision algorithms and datasets for pig husbandry and management tasks; (iii) the process of dataset
construction for computer vision algorithm development. In this appraisal, we outline common difficulties and
challenges associated with each of these themes and suggest possible solutions. Finally, we highlight the opportunities for future research in computer vision applications that can build upon existing knowledge of pig
management by extending our capability to interpret pig behaviors and thereby overcome the current barriers to
applying computer vision technologies to pig production systems. In conclusion, we believe productive collaboration between animal-based scientists and computer-based scientists may accelerate animal behavior studies
and lead the computer vision technologies to commercial applications in pig production facilities.
keywords: بینایی کامپیوتر | دامپروری دقیق | رفتار - اخلاق | یادگیری عمیق | مجموعه داده | گراز | Computer vision | Precision livestock farming | Behavior | Deep learning | Dataset | Swine |
مقاله انگلیسی |