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ردیف | عنوان | نوع |
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1 |
Natural Embedding of the Stokes Parameters of Polarimetric Synthetic Aperture Radar Images in a Gate-Based Quantum Computer
جاسازی طبیعی پارامترهای استوکس تصاویر رادار دیافراگم مصنوعی قطبی در یک کامپیوتر کوانتومی مبتنی بر گیت-2022 Quantum algorithms are designed to process quantum data (quantum bits) in a gate-based quantum computer. They
are proven rigorously that they reveal quantum advantages over
conventional algorithms when their inputs are certain quantum
data or some classical data mapped to quantum data. However,
in a practical domain, data are classical in nature, and they are
very big in dimension, size, and so on. Hence, there is a challenge
to map (embed) classical data to quantum data, and even no
quantum advantages of quantum algorithms are demonstrated
over conventional ones when one processes the mapped classical
data in a gate-based quantum computer. For the practical domain
of earth observation (EO), due to the different sensors on remotesensing platforms, we can map directly some types of EO data
to quantum data. In particular, we have polarimetric synthetic
aperture radar (PolSAR) images characterized by polarized
beams. A polarized state of the polarized beam and a quantum
bit are the Doppelganger of a physical state. We map them to
each other, and we name this direct mapping a natural embedding,
otherwise an artificial embedding. Furthermore, we process our
naturally embedded data in a gate-based quantum computer by
using a quantum algorithm regardless of its quantum advantages
over conventional techniques; namely, we use the QML network
as a quantum algorithm to prove that we naturally embedded
our data in input qubits of a gate-based quantum computer.
Therefore, we employed and directly processed PolSAR images
in a QML network. Furthermore, we designed and provided a
QML network with an additional layer of a neural network,
namely, a hybrid quantum-classical network, and demonstrate
how to program (via optimization and backpropagation) this
hybrid quantum-classical network when employing and processing PolSAR images. In this work, we used a gate-based quantum
computer offered by an IBM Quantum and a classical simulator
for a gate-based quantum computer. Our contribution is that
we provided very specific EO data with a natural embedding
feature, the Doppelganger of quantum bits, and processed them
in a hybrid quantum-classical network. More importantly, in the
future, these PolSAR data can be processed by future quantum
algorithms and future quantum computing platforms to obtain
(or demonstrate) some quantum advantages over conventional
techniques for EO problems.
Index Terms: Natural embedding | parameterized quantum circuit | polarimetric synthetic aperture radar (PolSAR) | quantum machine learning (QML). |
مقاله انگلیسی |
2 |
Quantum Embedding Search for Quantum Machine Learning
جستجوی توکار کوانتومی برای یادگیری ماشین کوانتومی-2022 This paper introduces an automated search algorithm (QES, pronounced as ‘‘quest’’), which
derives optimal design of entangling layout for supervised quantum machine learning. First, we establish
the connection between the structures of entanglement using CNOT gates and the representations of
directed multi-graphs, enabling a well-defined search space. The proposed encoding scheme of quantum
entanglement as genotype vectors bridges the ansatz optimization and classical machine learning, allowing
efficient search on any well-defined search space. Second, we instigate the entanglement level to reduce
the cardinality of the search space to a feasible size for practical implementations. Finally, we mitigate the
cost of evaluating the true loss function by using surrogate models via sequential model-based optimization.
We demonstrate the feasibility of our proposed approach on simulated and bench-marking datasets, including
Iris, Wine and Breast Cancer datasets, which empirically shows that found quantum embedding architecture
by QES outperforms manual designs in term of the predictive performance.
INDEX TERMS: Ansatz optimization | quantum embeddings | quantum machine learning | quantum logic gates | quantum neural network | quantum computing. |
مقاله انگلیسی |
3 |
An IoT-enabled intelligent automobile system for smart cities
یک سیستم خودروی هوشمند مجهز به اینترنت اشیا برای شهرهای هوشمند-2022 In our world of advancing technologies, automobiles are one industry where we can see
improved ergonomics and feature progressions. Artificial Intelligence (AI) integrated with
Internet of Things (IoT) is the future of most of the cutting-edge applications developed
for automobile industry to enhance performance and safety. The objective of this research
is to develop a new feature that can enhance the existing technology present in automo-
biles at low-cost. We had previously developed a technology known as Smart Accident
Precognition System (SAPS) which reduces the rate of accidents in automobile and also
enhance the safety of the passengers. Current research advances this technique by inte-
grating Google Assistant with the SAPS. The proposed system integrates several embedded
devices in the automobiles that monitor various aspects such as speed, distance, safety
measures like seatbelt, door locks, airbags, handbrakes etc. The real-time data is stored in
the cloud and the vehicle can adapt to various situations from the previous data collected.
Also, with the Google Assistant user can lock and unlock, start and stop, alert and do var-
ious automated tasks such as low fuel remainder, insurance remainders etc. The proposed
IoT enabled real-time vehicle system can detect accidents and adapt to change according
to various conditions. Further, with RFID keyless entry authentication the vehicle is secure
than ever before. This proposed system is much efficient to the existing systems and will
have a great positive impact in the automobile industry and society.
© 2020 Elsevier B.V. All rights reserved. keywords: هوش مصنوعی | سیستم هوشمند خودرو | اینترنت اشیا | شهرهای هوشمند | سیستم هوشمند | Artificial intelligence | Intelligent automobile system | Internet of Things | Smart Cities | Smart System |
مقاله انگلیسی |
4 |
Designing and constructing internet-of-Things systems: An overview of the ecosystem
طراحی و ساخت سیستم های اینترنت اشیا: مروری بر اکوسیستم-2022 The current complexity of IoT systems and devices is a barrier to reach a healthy ecosystem,
mainly due to technological fragmentation and inherent heterogeneity. Meanwhile, the field
has scarcely adopted any engineering practices currently employed in other types of large-scale
systems. Although many researchers and practitioners are aware of the current state of affairs
and strive to address these problems, compromises have been hard to reach, making them
settle for sub-optimal solutions. This paper surveys the current state of the art in designing
and constructing IoT systems from the software engineering perspective, without overlooking
hardware concerns, revealing current trends and research directions.
keywords: اینترنت اشیا | مهندسی نرم افزار | سیستم های جاسازی شده | سیستم های در مقیاس بزرگ | طراحی سیستم | توسعه سیستم | Internet-of-Things | Softwareengineering | Embeddedsystems | Large-scalesystems | Systemdesign | Systemdevelopment |
مقاله انگلیسی |
5 |
A verb-frame frequency account of constraints on long-distance dependencies in English
یک حساب فرکانس فعل از محدودیتهای وابستگیهای فاصلهای طولانی در زبان انگلیسی-2021 Going back to Ross (1967) and Chomsky (1973), researchers have sought to understand what conditions permit
long-distance dependencies in language, such as between the wh-word what and the verb bought in the sentence
‘What did John think that Mary bought?’. In the present work, we attempt to understand why changing the main
verb in wh-questions affects the acceptability of long-distance dependencies out of embedded clauses. In
particular, it has been claimed that factive and manner-of-speaking verbs block such dependencies (e.g., ‘What
did John know/whisper that Mary bought?’), whereas verbs like think and believe allow them. Here we provide 3
acceptability judgment experiments of filler-gap constructions across embedded clauses to evaluate four types of
accounts based on (1) discourse; (2) syntax; (3) semantics; and (4) our proposal related to verb-frame frequency.
The patterns of acceptability are most simply explained by two factors: verb-frame frequency, such that de-
pendencies with verbs that rarely take embedded clauses are less acceptable; and construction type, such that
wh-questions and clefts are less acceptable than declaratives. We conclude that the low acceptability of filler-gap
constructions formed by certain sentence complement verbs is due to infrequent linguistic exposure. keywords: پردازش حکم | اثرات فرکانس | وابستگی های راه دور | جزایر نحوی | Sentence processing | Frequency effects | Long-distance dependencies | Syntactic islands |
مقاله انگلیسی |
6 |
Action recognition of dance video learning based on embedded system and computer vision image
تشخیص عمل یادگیری ویدئویی رقص بر اساس سیستم تعبیه شده و تصویر بینایی ماشین-2021 Extraction and unfettered online / offline video sequence to identify complex human activity is computer vision a challenging task. To presents the classification of Indian classical dance moves using the powerful features of embedded system tools: Field Programmable Gate Array (FPGA). In this work, the Indian classical dance video for human action recognition is, YouTube data from offline and online control audio and video recordings of live performances carried out. Handprint create offline data with ten different themes familiar dance of 200 m / from various Indian classical dance forms in the context of a variety of poses. Online data collection dance ten different subjects from YouTube. Each dance posture is occupied 60 or video in both cases. FPGA training and 8 different sample dimensions, each performed by a plurality of sets of subject. The remaining two samples for testing the trained FPGA. Different FPGA architecture design, and with our test data in order to obtain better recognition accuracy. Compared the report on the same data set and other classification model to achieve a 90% recognition rate. Keywords: Field-programmable gate array (FPGA) | Learning action recognition | Embedded system tool |
مقاله انگلیسی |
7 |
Research on prepaid account financing model based on embedded system and Internet of Things
تحقیق در مورد مدل تامین مالی پیش پرداخت بر اساس سیستم جاسازی شده و اینترنت اشیا-2021 Internet of Things (IoT) network interconnection to create objects and things will play the Internet to play an
active role in the global network in the future. For the Internet of Things, which is widely adopted through
funding models, it must be trusted in the IoT security infrastructure. Efficiently and Securely IoT is very
important to define how each other can communicate with remote servers and get Exchange account informa-
tion. Prepayments for effective financial management and an important choice for financial IoT for service
providers and customers. However, it must be supported by real-time credit checking and costing. Internet re-
sources are consumed by these real-time action stuff providers and impose high costs on the old system. To solve
this problem, to propose the K Means Algorithm scalable accounting solutions, where the user is hosted each
occupies a prepaid account, constitute the components of embedded systems. Based on each of our prepaid
billing components’ supervision, it is at the same time consumed by the embedded system of all services, based
on the calculation of the service packages consumed by the customer. Prepaid accounts are reassigned when the
customer had sufficient credit to supplement their use and are allocated based on IoT services’ consumption. This
work aims to reduce the cost of pre-paid services and ensure that service delivery is not to interfere with the
charging unit. Also, embedded systems’ theoretical and experimental analysis shows that this work can store
long-lived services on the Internet of Things to provide inexpensive accounting solutions.
keywords: الگوریتم میانگین کا | سیستم های جاسازی شده | اینترنت اشیا | مدیریت مالی | سیستم حسابداری پیش پرداخت | K means algorithm | Embedded systems | Internet of Things | Financial management | Prepaid accounting system |
مقاله انگلیسی |
8 |
Benchmarking vision kernels and neural network inference accelerators on embedded platforms
محک زدن هسته بینایی و شتاب دهنده های استنتاج شبکه عصبی بر روی سیستم عامل های توکار-2021 Developing efficient embedded vision applications requires exploring various algorithmic optimization trade- offs and a broad spectrum of hardware architecture choices. This makes navigating the solution space and finding the design points with optimal performance trade-offs a challenge for developers. To help provide a fair baseline comparison, we conducted comprehensive benchmarks of accuracy, run-time, and energy efficiency of a wide range of vision kernels and neural networks on multiple embedded platforms: ARM57 CPU, Nvidia Jetson TX2 GPU and Xilinx ZCU102 FPGA. Each platform utilizes their optimized libraries for vision kernels (OpenCV, Vision Works and xfOpenCV) and neural networks (OpenCV DNN, TensorRT and Xilinx DPU). Forvision kernels, our results show that the GPU achieves an energy/frame reduction ratio of 1.1–3.2× compared to the others for simple kernels. However, for more complicated kernels and complete vision pipelines, the FPGA outperforms the others with energy/frame reduction ratios of 1.2–22.3×. For neural networks [Inception-v2 and ResNet-50, ResNet-18, Mobilenet-v2 and SqueezeNet], it shows that the FPGA achieves a speed up of [2.5, 2.1, 2.6, 2.9 and 2.5]× and an EDP reduction ratio of [1.5, 1.1, 1.4, 2.4 and 1.7]× compared to the GPU FP16 implementations, respectively. Keywords: Benchmarks | CPUs | GPUs | FPGAs | Embedded vision | Neural networks |
مقاله انگلیسی |
9 |
Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management
ادغام تجزیه و تحلیل داده های بزرگ جاسازی شده معماری شهر هوشمند با وب سایت RESTful برای ارائه خدمات کارآمد و مدیریت انرژی-2020 Emergence of smart things has revolutionized the conventional internet into a connected network of
things, maturing the concept of Internet of Things (IoT). With the evolution of IoT, many attempts were
made to realize the notion of smart cities. However, demands for processing enormous amount of data
and platform incompatibilities of connected smart things hindered the actual implementation of smart
cities. Keeping it in view, we proposed a Big Data analytics embedded smart city architecture, which
is further integrated with the web via a smart gateway. Integration with the web provides a universal
communication platform to overcome the platform incompatibilities of smart things. We introduced Big
Data analytics to enhance data processing speed. Further, we evaluated authentic datasets to determine
the threshold values for intelligent decision-making and to present the performance improvement gained
in data processing. Finally, we presented a representational state transfer (RESTful) web of things (WoT)
integrated smart building architecture (smart home) to reveal the performance improvements of the
proposed smart city architecture in terms of network performance and energy management of smart
buildings. Keywords: Smart city | Big Data analytics | Smart home | Web of things | RESTful architecture |
مقاله انگلیسی |
10 |
AITIA: Embedded AI Techniques for Embedded Industrial Applications
AITIA: تکنیک های هوش مصنوعی جاسازی شده برای کاربردهای صنعتی جاسازی شده-2020 New achievements in Artificial Intelligence (AI)
and Machine Learning (ML) are reported almost daily by the
big companies. While those achievements are accomplished by
fast and massive data processing techniques, the potential of
embedded machine learning, where intelligent algorithms run
in resource-constrained devices rather than in the cloud, is still
not understood well by the majority of the industrial players
and Small and Medium Entereprises (SMEs). Nevertheless,
the potential embedded machine learning for processing highperformance
algorithms without relying on expensive cloud solutions
is perceived as very high. This potential has led to a broad
demand by industry and SMEs for a practical and applicationoriented
feasibility study, which helps them to understand the
potential benefits, but also the limitations of embedded AI. To
address these needs, this paper presents the approach of the
AITIA project, a consortium of four Universities which aims at
developing and demonstrating best practices for embedded AI
by means of four industrial case studies of high-relevance to the
European industry and SMEs: sensors, security, automotive and
industry 4.0. Index Terms: artificial intelligence | machine learning | embedded hardware | sensors | network intrusion detection | driver assistance | industry 4.0 |
مقاله انگلیسی |