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ردیف | عنوان | نوع |
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11 |
Efficient Hardware Implementation of Finite Field Arithmetic AB + C for Binary Ring-LWE Based Post-Quantum Cryptography
اجرای سخت افزار کارآمد محاسبات میدان محدود AB + C برای رمزنگاری پس کوانتومی مبتنی بر حلقه باینری-LWE-2022 Post-quantum cryptography (PQC) has gained significant attention from the community
recently as it is proven that the existing public-key cryptosystems are vulnerable to the attacks launched from
the well-developed quantum computers. The finite field arithmetic AB þ C, where A and C are integer polynomials and B is a binary polynomial, is the key component for the binary Ring-learning-with-errors (BRLWE)-
based encryption scheme (a low-complexity PQC suitable for emerging lightweight applications). In this paper,
we propose a novel hardware implementation of the finite field arithmetic AB þ C through three stages of interdependent efforts: (i) a rigorous mathematical formulation process is presented first; (ii) an efficient hardware
architecture is then presented with detailed description; (iii) a thorough implementation has also been given
along with the comparison. Overall, (i) the proposed basic structure (u ¼ 1) outperforms the existing designs,
e.g., it involves 55.9% less area-delay product (ADP) than [13] for n ¼ 512; (ii) the proposed design also offers
very efficient performance in time-complexity and can be used in many future applications.
INDEX TERMS: Binary ring-learning-with-errors | finite field arithmetic | FPGA platform | hardware design | post-quantum cryptography |
مقاله انگلیسی |
12 |
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 |
13 |
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 |
14 |
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 |
15 |
Enabling Pulse-Level Programming, Compilation, and Execution in XACC
فعال کردن برنامه نویسی، کامپایل و اجرا در سطح پالس در XACC-2022 Noisy gate-model quantum processing units (QPUs) are currently available from vendors over the cloud, and digital
quantum programming approaches exist to run low-depth circuits on physical hardware. These digital representations are ultimately
lowered to pulse-level instructions by vendor quantum control systems to affect unitary evolution representative of the submitted digital
circuit. Vendors are beginning to open this pulse-level control system to the public via specified interfaces. Robust programming
methodologies, software frameworks, and backend simulation technologies for this analog model of quantum computation will prove
critical to advancing pulse-level control research and development. Prototypical use cases for this include error mitigation, optimal
pulse control, and physics-inspired pulse construction. Here we present an extension to the XACC quantum-classical software
framework that enables pulse-level programming for superconducting, gate-model quantum computers, and a novel, general, and
extensible pulse-level simulation backend for XACC that scales on classical compute clusters via MPI. Our work enables custom
backend Hamiltonian definitions and gate-level compilation to available pulses with a focus on performance and scalability. We end with
a demonstration of this capability, and show how to use XACC for pertinent pulse-level programming tasks.
Index Terms: Quantum computing | quantum programming models | quantum control | quantum simulation |
مقاله انگلیسی |
16 |
Tuning of grayscale computer vision systems
تنظیم سیستم های بینایی کامپیوتری در مقیاس خاکستری-2022 Computer vision systems perform based on their design and parameter setting. In computer vision systems
that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of
the systems in terms of both results quality and computational costs. Appropriate setting of the weights for
the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision
system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or
no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the
grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the
system and subsystem to small changes in the distribution of data in a color space of the processed images.
We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the
advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show
that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies.
The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer
vision system with the robustness of its performance against variable input data.
keywords: Computer vision | Parameter optimization | Performance evaluation | WECIA graph | Weighted means grayscale conversion |
مقاله انگلیسی |
17 |
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 |
18 |
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 |
19 |
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 |
20 |
Generation of Truly Random Numbers on a Quantum Annealer
تولید اعداد واقعا تصادفی در یک آنیلر کوانتومی-2022 This study investigates how qubits of modern quantum annealers (QA) such as D-Wave can be
applied for generating truly random numbers. We show how a QA can be initialised and how the annealing
schedule can be set so that after the annealing, thousands of truly random binary numbers are measured in
parallel. Those can then be converted to uniformly distributed natural or real numbers in desired ranges,
either biased or unbiased. We discuss the observed qubits’ properties and their influence on the random
number generation and consider various physical factors that influence the performance of our generator,
i.e., digital-to-analogue quantisation errors, flux errors, temperature errors and spin bath polarisation. The
numbers generated by the proposed algorithm successfully pass various tests on randomness from the NIST
test suite. Our source code and large sets of truly random numbers will be made publicly available on our
project web page.
INDEX TERMS: Random numbers | true randomness test | superconducting flux qubits | quantum annealing | QUBO |
مقاله انگلیسی |