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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 |
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 |
3 |
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 |
4 |
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 |
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 |
Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022 Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and
mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-
scale topographical information. This paper addresses this issue by providing a novel method for evaluating
surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging)
measurements. The developed method derives topographical properties and runoff accumulation by applying a
semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology
technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the
SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the
proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales
and identify problematic low points that could be susceptible to water ponding. Municipalities and property
owners can use this information to take targeted corrective maintenance actions. keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure |
مقاله انگلیسی |
7 |
Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps
به سمت مدیریت خودکار ظروف زباله در شهرها از طریق بینایی کامپیوتری: محلی سازی ظروف و موقعیت جغرافیایی در نقشه های شهر-2022 This paper describes the scientific achievements of a collaboration between a research group and the waste
management division of a company. While these results might be the basis for several practical or commercial
developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo-
located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers
and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an
automatic discrimination between images with and without containers. More precisely, two state-of-the-art
object detectors based on deep learning techniques have been selected for testing, according to their perfor-
mance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental
results indicate that the proposed visual model for waste container detection is able to effectively operate with
consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,…) and
the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of
size, climate, urban layout and containers’ appearance. keywords: Waste container localization | Deep Learning | Computer Vision | Object detection |
مقاله انگلیسی |
8 |
Managing a Quantum Computing Team—Insights and Challenges at Itaú Unibanco
مدیریت یک تیم محاسبات کوانتومی - بینش و چالش ها در Itau Unibanco-2022 Quantum computing technology is developing rapidly. Technology
managers are now faced with pressure to bring the emerging technology inhouse, albeit perhaps stealthy. This article presents the story of a large
Brazilian financial services firm that is facing the quantum “opportunity” headon. We provide insights into the challenge of introducing nonproven
technology to this one particular organization. In addition to team-level
management, other aspects in the realm of organization-level issues are
presented including expectation setting, reporting, and budgeting.
keywords: Diffusion of ITT innovations | entrepreneurship | information and telecommunication technologies | quantum computing |
مقاله انگلیسی |
9 |
Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022 Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In
recent years, social media data have become a promising data source for the assessment of cultural ecosystem
services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the
CES and landscape features from social media photos by manual content analysis. In addition, relatively few
studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this
study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition,
image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud
Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and
landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that
the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’
photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes
(corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated
image analysis on social media photos. We also noticed that tourists paid more attention to the place identity
featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants,
flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and
CES assessment, which could provide insights for urban park planning and tourism management. keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision |
مقاله انگلیسی |
10 |
On the Logical Error Rate of Sparse Quantum Codes
در مورد میزان خطای منطقی کدهای کوانتومی پراکنده-2022 The quantum paradigm presents a phenomenon known as degeneracy that can potentially
improve the performance of quantum error correcting codes. However, the effects of this mechanism are
sometimes ignored when evaluating the performance of sparse quantum codes and the logical error rate is
not always correctly reported. In this article, we discuss previously existing methods to compute the logical
error rate and we present an efficient coset-based method inspired by classical coding strategies to estimate
degenerate errors and distinguish them from logical errors. Additionally, we show that the proposed method
presents a computational advantage for the family of Calderbank–Shor–Steane codes. We use this method
to prove that degenerate errors are frequent in a specific family of sparse quantum codes, which stresses
the importance of accurately reporting their performance. Our results also reveal that the modified decoding
strategies proposed in the literature are an important tool to improve the performance of sparse quantum
codes.
INDEX TERMS: Iterative decoding | quantum error correction (QEC) | quantum low density generator matrix codes | quantum low-density parity check (QLDPC) codes. |
مقاله انگلیسی |