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ردیف | عنوان | نوع |
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1 |
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 |
2 |
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 |
3 |
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 |
4 |
Fuzzy Logic on Quantum Annealers
منطق فازی در آنیل های کوانتومی-2022 Quantum computation is going to revolutionize the world of
computing by enabling the design of massive parallel algorithms that solve
hard problems in an efficient way, thanks to the exploitation of quantum
mechanics effects, such as superposition, entanglement, and interference.
These computational improvements could strongly influence the way how
fuzzy systems are designed and used in contexts, such as Big Data, where
computational efficiency represents a nonnegligible constraint to be taken
into account. In order to pave the way toward this innovative scenario,
this article introduces a novel representation of fuzzy sets and operators
based on quadratic unconstrained binary optimization problems, so as to
enable the implementation of fuzzy inference engines on a type of quantum
computers known as quantum annealers.
Index Terms: Fuzzy logic | quantum computing | simulated annealing. |
مقاله انگلیسی |
5 |
Learning Quantum Drift-Diffusion Phenomenon by Physics-Constraint Machine Learning
یادگیری پدیده رانش کوانتومی- انتشار با یادگیری ماشین محدودیت فیزیک-2022 Recently, deep learning (DL) is widely used to
detect physical phenomena and has obtained encouraging results.
Several works have shown that it can learn quantum phenomenon. Subsequently, quantum machine learning (QML) has
been paid more attention by academia and industry. Quantum
drift-diffusion (QDD) is a commonplace physical phenomenon,
which is a macroscopic description of electrons and holes in
a semiconductor. They are commonly used to attain an understanding of the property of semiconductor devices in physics
and engineering. We are motivated by the relaxation-time limit
from the quantum-Navier-Stokes-Poisson system (QNSP) to the
QDD equation and the existence of finite energy weak solutions
to the QDD equation has been proved. Therefore, in this work,
the quantum drift-diffusion learning neural network (QDDLNN)
is proposed to investigate the quantum drift phenomena from
limited observations. Furthermore, a piece of numerical evidence
is found that the NNs can describe quantum transport phenomena by simulating the quantum confinement transport equationquantum Navier-Stokes equation.
Index Terms: Quantum machine learning | quantum drift diffusion | physical-information learning | quantum transport | quantum fluid model. |
مقاله انگلیسی |
6 |
Mobile Control Plane Design for Quantum Satellite Backbones
طراحی هواپیمای کنترل سیار برای ستون فقرات ماهواره ای کوانتومی-2022 The interconnection of quantum computers
through the so-called Quantum Internet is a very
promising approach.
The most critical issues concern the physical
layer, considering that the creation of entanglement over long distances is still problematic.
Given the difficulty that usually arises from fiber
optics due to exponential losses, the introduction of intermediate quantum repeaters (QRs)
allows mitigating the problem. A quantum satellite network based on QRs on satellites deployed
over low Earth orbit could make it possible to
overcome the constraints of terrestrial optical
networks. Hence, the recent technological developments in terms of quantum satellite communications motivated our investigation on an ad
hoc quantum satellite backbone design based on
the software defined networking paradigm with a
control plane directly integrated into the constellation itself. Our aim is to outline some guidelines
by comparing several options. Specifically, the
focus is to analyze different architectural solutions
making some considerations on their feasibility,
possible benefits, and costs. Finally, we performed
some simulations on the architectures we considered the most promising, concluding that the integration of the control plane in the constellation
itself is the most appropriate solution.
keywords: |
مقاله انگلیسی |
7 |
Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs
رمزگشاهای شبکه عصبی برای تصحیح خطای کوانتومی با استفاده از کدهای سطحی: کاوش فضایی مبادلات هزینه و عملکرد سخت افزار-2022 Quantum error correction (QEC) is required in quantum computers to mitigate the effect of
errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most
computationally expensive task in the classical electronic back-end. Decoders employing neural networks
(NN) are well-suited for this task but their hardware implementation has not been presented yet. This work
presents a space exploration of fully connected feed-forward NN decoders for small distance surface codes.
The goal is to optimize the NN for the high-decoding performance, while keeping a minimalistic hardware
implementation. This is needed to meet the tight delay constraints of real-time surface code decoding. We
demonstrate that hardware-based NN-decoders can achieve the high-decoding performance comparable to
other state-of-the-art decoding algorithms whilst being well below the tight delay requirements (≈ 440 ns)
of current solid-state qubit technologies for both application-specific integrated circuit designs (<30 ns) and
field-programmable gate array implementations (<90 ns). These results indicate that NN-decoders are viable
candidates for further exploration of an integrated hardware implementation in future large-scale quantum
computers.
INDEX TERMS: Application-specific integrated circuit (ASIC) | complementary metal-oxide semiconductor (CMOS) | CMOS integrated circuits | combinational circuits | cryo-CMOS decoding | cryogenic electronics | digital integrated circuits, error correction codes | feedforward neural networks (NNs) | field programmable gate array (FPGA) | fixed-point arithmetic | machine learning NNs | pareto analysis | quantum computing | quantum-error-correction (QEC) codes | supervised learning, surface codes (SCs). |
مقاله انگلیسی |
8 |
برهم کنش متقابل جهت گیری ها نشاندهنده رمز گشایی سطح بالا به پایین در حافظه کار بصری است
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 41 کدگذاری حسی ( چگونه محرکها واکنشهای حسی را برمیانگیزد ) به پیشرفت از ویژگیهای سطح پایین به سطح بالا مشهور است .
رمزگشایی ( چگونه پاسخها منجر به ادراک میشود ) کمتر درک میشود اما اغلب فرض میشود که از همان سلسلهمراتب پیروی میکند .
بر این اساس ، رمز گشایی جهت گیری باید در نواحی سطح پایین مانند V۱ ، بدون برهم کنش متقابل رخ دهد .
با این حال , یک مطالعه , دینگ ,کوا , تی سودیکس , و کان ( 2017 ) شواهدی در برابر این فرض ارائه دادند و پیشنهاد کردند که رمزگشایی بصری اغلب ممکن است از سلسلهمراتب سطح بالا به پایین در حافظه کاری پیروی کند , که در آن محدودیتهای سطح به پایین تعامل بین ویژگیهای سطح پایینتر را ایجاد میکند . اگر دو جهت گیری در جهت مخالف تثبیت هر دو عملی هستند و حافظه فعال را وارد میکنند , پس باید با هم تعامل داشته باشند. ما در واقع هم برهم کنش متقابل پیشبینیشده ( تنفر و همبستگی ) بین جهت گیری ها را پیدا کردیم .
آزمایشها کنترل و تجزیه و تحلیلهای کنترلی , توضیحات دیگری همچون تعصب گزارش دهی و انطباق در سراسر آزمایشها در همان سمت تثبیت را رد کردند . به علاوه , ما دادهها را با استفاده از چارچوب رمزگشایی Bayesian سطح پایین به سطح پایین توضیح دادیم .
واژه های کاربردی: کدگشایی بصری | جانبداری | سر و صدا | بیزین گذشته نگر |
مقاله ترجمه شده |
9 |
Simultaneous Estimation of Parameters and the State of an Optical Parametric Oscillator System
تخمین همزمان پارامترها و وضعیت یک سیستم نوسان ساز پارامتری نوری-2022 In this article, we consider the filtering problem of an optical parametric oscillator (OPO).
The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the
system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously.
We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption
of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter
method are employed to simultaneously estimate the system state and the pump power. Numerical examples
demonstrate the effectiveness of the proposed algorithms. keywords: Optical parametric oscillator (OPO) | OPO system | parameter estimation | quantum state estimation | simultaneous estimation. |
مقاله انگلیسی |
10 |
Timing and Resource-Aware Mapping of Quantum Circuits to Superconducting Processors
نگاشت زمان بندی و آگاهی از منابع مدارهای کوانتومی به پردازنده های ابررسانا-2022 Quantum algorithms need to be compiled to respect
the constraints imposed by quantum processors, which is known
as the mapping problem. The mapping procedure will result in an
increase of the number of gates and of the circuit latency, decreasing the algorithm’s success rate. It is crucial to minimize mapping
overhead, especially for noisy intermediate-scale quantum (NISQ)
processors that have relatively short qubit coherence times and
high gate error rates. Most of prior mapping algorithms have only
considered constraints, such as the primitive gate set and qubit
connectivity, but the actual gate duration and the restrictions
imposed by the use of shared classical control electronics have
not been taken into account. In this article, we present a mapper
called Qmap to make quantum circuits executable on scalable
processors with the objective of achieving the shortest circuit
latency. In particular, we propose an approach to formulate the
classical control restrictions as resource constraints in a conventional list scheduler with polynomial complexity. Furthermore,
we implement a routing heuristic to cope with the connectivity limitation. This router finds a set of movement operations
that minimally extends circuit latency. To analyze the mapping
overhead and evaluate the performance of different mappers, we
map 56 quantum benchmarks onto a superconducting processor named Surface-17. Compared to a prior mapping strategy
that minimizes the number of operations, Qmap can reduce the
latency overhead (LtyOH) up to 47.3% and operation overhead
up to 28.6%, respectively.
Index Terms—Quantum compilation | quantum computing | resource-constrained scheduling | routing. |
مقاله انگلیسی |