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ردیف | عنوان | نوع |
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31 |
Efficient Implementation of Lightweight Hash Functions on GPU and Quantum Computers for IoT Applications
اجرای کارآمد توابع هش سبک در GPU و کامپیوترهای کوانتومی برای کاربردهای اینترنت اشیا-2022 Secure communication is important for Internet of Things (IoT) applications, to avoid cybersecurity attacks. One of the key security aspects is data integrity, which can be protected by employing cryptographic hash functions. Recently, US National Institute of Standards and Technology (NIST)
announced a competition to standardize lightweight hash functions, which can be used in IoT applications.
IoT communication involves various hardware platforms, from low-end microcontrollers to high-end cloud
servers with GPU accelerators. Since many sensor nodes are connected to the gateway devices and cloud
servers, performing high throughput integrity check is important to secure IoT applications. However, this is a
time consuming task even for high-end servers, which may affect the response time in IoT systems. Moreover,
no prior work had evaluated the performance of NIST candidates on contemporary processors like GPU and
quantum computers. In this study, we showed that with carefully crafted implementation techniques, all
the finalist hash function candidates in the NIST standardization competition can achieve high throughput
(up-to 1,000 Gbps) on a RTX 3080 GPU. This research output can be used by IoT gateway devices and cloud
servers to perform data integrity checks at high speed, thus ensuring a timely response. In addition, this is
also the first study that showcase the implementation of NIST lightweight hash functions on a quantum
computer (ProjectQ). Besides securing the communication in IoT, these efficient implementations on a GPU
and quantum computer can be used to evaluate the strength of respective hash functions against brute-force
attack.
INDEX TERMS: Graphics processing units (GPU) | hash function | lightweight cryptography | quantum computer. |
مقاله انگلیسی |
32 |
A robust structural vibration recognition system based on computer vision
یک سیستم قوی تشخیص ارتعاش ساختاری بر اساس بینایی کامپیوتری-2022 Vibration-based structural health monitoring (SHM) systems are useful tools for assessing structural safety performance quantitatively. When employing traditional contact sensors, achieving high-resolution spatial measurements for large-scale structures is challenging, and fixed contact sensors may also lose dependability when the lifetime of the host structure is surpassed. Researchers have paid close attention to computer vision because it is noncontact, saves time and effort, is inexpensive, and has high efficiency in giving visual perception. In advanced noncontact measurements, digital cameras can capture the vibration information of structures remotely and swiftly. Thus, this work studies a system for recognizing structural vibration. The system ensures acquiring high-quality structural vibration signals by the following: 1) Establishing a novel image preprocessing, which includes visual partitioning measurement and image enhancement techniques; 2) initial recognition of structural vibration using phase-based optical flow estimation (POFE), which introduces 2-D Gabor wavelets to extract the independent phase information of the image to track the natural texture targets on the surface of the structure; 3) extracting the practical vibration information of the structure using mode decomposition to remove the complex environment of the camera vibration and other noises; 4) employing phase-based motion magnification (PMM) techniques to magnify small vibration signals, and then recognizing the complete information on the vibration time range of the structure. The research results of the laboratory experiments and field testing conducted under three different cases reveal that the system can recognize structural vibration in complicated environments.
keywords: Computer vision | Phase | Motion estimation | Motion magnification | Mode decomposition | Structural vibration |
مقاله انگلیسی |
33 |
Efficient Quantum Blockchain With a Consensus Mechanism QDPoS
بلاک چین کوانتومی کارآمد با مکانیزم اجماع QDPoS-2022 Quantum blockchain is expected to offer an alternative to classical blockchain to resist malicious attacks laughed by future quantum computers. Although a few quantum blockchain schemes have been constructed, their efficiency is low and unable to meet application requirements due to the fact that they lack of a suitable consensus mechanism. To tackle this issue, a consensus mechanism called quantum delegated proof of stake (QDPoS) is constructed by using quantum voting to provide fast decentralization for the quantum blockchain scheme at first. Then an efficient scheme is proposed for quantum blockchain based on QDPoS, where the classical information is initialized as a part of each single quantum state and these quantum states are entangled to form the chain. Compared with previous methods, the designed quantum blockchain scheme is more complete and carried out with higher efficiency, which greatly contributes to better adapting to the challenges of the quantum era.
Index Terms: Quantum blockchain | consensus mechanism | QDPoS | quantum voting | quantum entanglement. |
مقاله انگلیسی |
34 |
Head tremor in cervical dystonia: Quantifying severity with computer vision
لرزش سر در دیستونی دهانه رحم: کمی کردن شدت با دید کامپیوتری-2022 Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective
observation. Technological developments offer alternatives for measuring HT severity that are objective and
amenable to automation.
Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-
based software system) to quantify oscillatory and directional aspects of HT from video recordings during a
clinical examination and to test its convergent validity with clinical rating scales.
Methods: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video
recordings from an examination segment in which participants were instructed to let their head drift to its most
comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used
Spearman’s correlation to measure the agreement between CMOR and clinical ratings.
Results: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis
was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants.
One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t =
12.89, p < 0.0001) HT axes. CMOR’s HT severity measure positively correlated with the HT item on the Toronto
Western Spasmodic Torticollis Rating Scale-2 (Spearman’s rho = 0.54, p < 0.001).
Conclusions: We demonstrate a new objective method to measure HT severity that requires only conventional
video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity
ratings. keywords: لرزش سر | ویدیو | بینایی کامپیوتر | درجه بندی شدت | TWSTRS | Head tremor | Video | Computer vision | Severity rating | TWSTRS |
مقاله انگلیسی |
35 |
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 |
36 |
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 |
37 |
Efficient Quantum State Preparation for the Cauchy Distribution Based on Piecewise Arithmetic
آماده سازی حالت کوانتومی کارآمد برای توزیع کوشی بر اساس حساب تکه ای-2022 The benefits of the quantum Monte Carlo algorithm heavily rely on the efficiency of the
superposition state preparation. So far, most reported Monte Carlo algorithms use the Grover–Rudolph state
preparation method, which is suitable for efficiently integrable distribution functions. Consequently, most reported works are based on log-concave distributions, such as normal distributions. However, non-log-concave
distributions still have many uses, such as in financial modeling. Recently, a new method was proposed
that does not need integration to calculate the rotation angle for state preparation. However, performing
efficient state preparation is still difficult due to the high cost associated with high precision and low error
in the calculation for the rotation angle. Many methods of quantum state preparation use polynomial Taylor
approximations to reduce the computation cost. However, Taylor approximations do not work well with
heavy-tailed distribution functions that are not bounded exponentially. In this article, we present a method
of efficient state preparation for heavy-tailed distribution functions. Specifically, we present a quantum
gate-level algorithm to prepare quantum superposition states based on the Cauchy distribution, which is a
non-log-concave heavy-tailed distribution. Our procedure relies on a piecewise polynomial function instead
of a single Taylor approximation to reduce computational cost and increase accuracy. The Cauchy distribution is an even function, so the proposed piecewise polynomial contains only a quadratic term and a constant
term to maintain the simplest approximation of an even function. Numerical analysis shows that the required
number of subdomains increases linearly as the approximation error decreases exponentially. Furthermore,
the computation complexity of the proposed algorithm is independent of the number of subdomains in the
quantum implementation of the piecewise function due to quantum parallelism. An example of the proposed
algorithm based on a simulation conducted in Qiskit is presented to demonstrate its capability to perform
state preparation based on the Cauchy distribution.
INDEX TERMS: Algorithms | gate operations | quantum computing. |
مقاله انگلیسی |
38 |
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 |
39 |
Eigen-Spectrum Estimation and Source Detection in a Massive Sensor Array Based on Quantum Assisted Hamiltonian Simulation Framework
تخمین طیف ویژه و تشخیص منبع در یک آرایه حسگر عظیم بر اساس چارچوب شبیهسازی همیلتونی به کمک کوانتومی-2022 In this work, we propose quantum assisted eigenvalue estimation and target detection algorithms for a large
sensor array via Hamiltonian simulation. Quantum algorithms
provide complexity advantage of a certain class of problems on
a quantum computer with fewer physical resources as compared
to their classical counterparts. The proposed algorithms make
use of the quantum phase estimation (QPE) as its core computing component. We have introduced an analytical quantum
framework to map from classical to quantum in the context of
target detection. Target detection involves an appropriate choice
of threshold based on the probability of detection or false alarm.
We exploited the massive sensor array structure and invoked
the random matrix theory to propose an optimal threshold.
It also takes into account the quantum measurement noise in the
framework. Numerical simulations are performed to ascertain
the efficacy of the proposed framework. The results suggest near
term applications of the quantum algorithm for large-scale linear
systems.
Index Terms: Quantum signal processing | quantum eigenvalue estimation | quantum phase estimation | Hamiltonian simulation | array signal processing. |
مقاله انگلیسی |
40 |
Spatiotemporal flow features in gravity currents using computer vision methods
ویژگی های جریان مکانی-زمانی در جریان های گرانشی با استفاده از روش های بینایی کامپیوتری-2022 Relationships between the features visually identified at the front of the flow’s current and parameters
regarding its velocity and turbulence were observed in early experimental works on the characterization of
gravity currents. Researches have associated front features, like lobes and clefts, with the flow’s turbulence, and
have used these associations ever since. In more recent works using numerical simulations, these connections
were still being validated for various flow parameters at higher front velocities. The majority of works regarding
measurements at the front of a gravity current rely on the front’s images for making its analysis and establish
relationships. Besides that, there is an interdisciplinary field related to computer science called computer vision,
devoted to study how digital images can be analyzed and how these results can be automated. This paper
describes the use of computer vision algorithms, particularly corner detection and optical flow, to automatically
track features at the front of gravity currents, either from physical or numerical experiments. To determine the
proposed approach’s accuracy, we establish a ground-truth method and apply it to numerical simulation results
data sets. The technique used to trace the front features along the flow showed promising results, especially
with higher Reynolds numbers flows.
keywords: جریان های گرانشی | ساختارهای لوب و شکاف | روش های کامپیوتری | ویژگی ردیابی | Gravitycurrents | Lobesandcleftsstructures | Computervisionmethods | Featurepointtracking |
مقاله انگلیسی |