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نتیجه جستجو - architectures

تعداد مقالات یافته شده: 219
ردیف عنوان نوع
1 DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks
DQRA: عامل مسیریابی کوانتومی عمیق برای مسیریابی درهم تنیده در شبکه های کوانتومی-2022
Quantum routing plays a key role in the development of the next-generation network system. In particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping among particles (e.g., photons) associated with nodes in the network. From another side of computing, machine learning has achieved numerous breakthrough successes in various application domains, including networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model for quantum networks that employs machine learning architectures to construct the routing path for the maximum number of demands (source–destination pairs) within a time window. Specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to accommodate the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
INDEX TERMS: Deep learning | deep reinforcement learning (DRL) | machine learning | next-generation network | quantum network routing | quantum networks.
مقاله انگلیسی
2 Moving towards intelligent telemedicine: Computer vision measurement of human movement
حرکت به سمت پزشکی از راه دور هوشمند: اندازه گیری بینایی کامپیوتری حرکت انسان-2022
Background: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID- 19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual judgement because video cameras are so ubiquitous in personal devices and new techniques, such as DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations. Objectives: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping, a validated test of human movement. Method: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak, a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101, ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking. Results: Over 96% (529/552) of DLC measures were within +∕−0.5 Hz of the Optotrak measures. At tapping frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with the laptop camera’s low FPS. Computer vision methods hold potential for moving us towards intelligent telemedicine by providing human movement analysis during consultations. However, further developments are required to accurately measure the fastest movements.
keywords: پزشکی از راه دور | ضربه زدن با انگشت | موتور کنترل | کامپیوتری | Telemedicine | DeepLabCut | Finger tapping | Motor control | Computer vision
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 Epsilon-Nets, Unitary Designs, and Random Quantum Circuits
شبکه های اپسیلون، طرح های واحد و مدارهای کوانتومی تصادفی-2022
Epsilon-nets and approximate unitary t-designs are natural notions that capture properties of unitary operations relevant for numerous applications in quantum information and quantum computing. In this work we study quantitative connections between these two notions. Specifically, we prove that, for d dimensional Hilbert space, unitaries constituting δ-approximate t-expanders form -nets for t d5/2 and δ 3d/2 d2. We also show that for arbitrary t, -nets can be used to construct δ-approximate unitary t-designs for δ t, where the notion of approximation is based on the diamond norm. Finally, we prove that the degree of an exact unitary t design necessary to obtain an -net must grow at least as fast as 1 (for fixed dimension) and not slower than d2 (for fixed ). This shows near optimality of our result connecting t-designs and nets. We apply our findings in the context of quantum computing. First, we show that that approximate t-designs can be generated by shallow random circuits formed from a set of universal twoqudit gates in the parallel and sequential local architectures considered in (Brandão et al., 2016). Importantly, our gate sets need not to be symmetric (i.e., contains gates together with their inverses) or consist of gates with algebraic entries. Second, we consider compilation of quantum gates and prove a nonconstructive Solovay-Kitaev theorem for general universal gate sets. Our main technical contribution is a new construction of efficient polynomial approximations to the Dirac delta in the space of quantum channels, which can be of independent interest.]
Index Terms: Unitary designs, epsilon nets | random quantum circuits | compilation of quantum gates | unitary channels.
مقاله انگلیسی
5 Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022
There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant of the successful delivery of site operations. Although manufacturers provide equipment performance hand- books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated image libraries are used to train and test several backbone architectures. Experimental results reveal the pre- cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of potentials to influence current practice of articulated machinery monitoring in projects.
keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures
مقاله انگلیسی
6 Mapping Nearest Neighbor Compliant Quantum Circuits Onto a 2-D Hexagonal Architecture
نگاشت مدارهای کوانتومی منطبق با نزدیکترین همسایه بر روی یک معماری دو بعدی شش ضلعی-2022
Quantum algorithms can be described as quantum circuits and are supposed to be carried out on an ideal quantum device that is far from current ones. The current quantum devices have a significant limitation on the connectivity between quantum bits. In other words, a quantum bit is only allowed to interact with its nearest neighbors (NNs). In reality, quantum bits have to be placed on a grid, where the connectivity between quantum bits is predefined. The predefined connectivity of a grid further determines the possible range of architectures of a quantum device after the placement of quantum bits. In this article, we propose to place quantum bits based on a 2-D hexagonal architecture rather than a 2-D Cartesian architecture. To validate the effectiveness, we leverage a workflow for mapping NN compliant quantum circuits onto targeting grids, where the workflow consists of a global reordering strategy and a local reordering strategy. With the advantages of the hexagonal grid, the overhead of making quantum circuits NN compliant is reduced significantly compared with the Cartesian grid. We also provide a comprehensive set of ablation analyses to gain a better understanding of the contributions of the components within our workflow. According to the experimental results, when changing the grid type from Cartesian to hexagonal, the global reordering strategy is crucial for small quantum circuits. In contrast, the local reordering strategy is more important than the global reordering strategy for large quantum circuits.
Index Terms: 2-D architecture | hexagonal grid | nearest neighbor (NN) compliant | quantum circuit.
مقاله انگلیسی
7 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
8 VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data
تجسم ها به عنوان بازنمایی های میانی (VLAIR): رویکردی برای به کارگیری بینایی کامپیوتری مبتنی بر یادگیری عمیق برای داده های غیر مبتنی بر تصویر-2022
Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or for personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.
keywords: تجسم اطلاعات | شبکه های عصبی کانولوشنال | تشخیص فعالیت های انسانی | خانه های هوشمند | بازنمایی داده ها | نمایندگی های میانی | تفسیر پذیری | یادگیری ماشین | یادگیری عمیق | Information visualization | Convolutional neural networks | Human activity recognition | Smart homes | Data representation | Intermediate representations | Interpretability | Machine learning | Deep learning
مقاله انگلیسی
9 Deep learning based computer vision approaches for smart agricultural applications
رویکردهای بینایی کامپیوتری مبتنی بر یادگیری عمیق برای کاربردهای کشاورزی هوشمند-2022
The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core of artificial intelligence, deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality. Computer vision techniques, in conjunction with high-quality image acquisition using remote cameras, enable non-contact and efficient technology-driven solutions in agriculture. This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting. Recent works in the area of computer vision were analyzed in this paper and categorized into (a) seed quality analysis, (b) soil analysis, (c) irrigation water management, (d) plant health analysis, (e) weed management (f) livestock management and (g) yield estimation. The paper also discusses recent trends in computer vision such as generative adversarial networks (GAN), vision transformers (ViT) and other popular deep learning architectures. Additionally, this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time. The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy. However, the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.
keywords: Agriculture automation | Computer vision | Deep learning | Machine learning | Smart agriculture | Vision transformers
مقاله انگلیسی
10 An exploration of local rules to map spawning processes to regular hardware architectures
کاوشی در قوانین محلی برای نگاشت فرآیندهای تخم ریزی به معماری های سخت افزاری معمولی-2022
This thesis presents an exploration of population growth via simulation in software to ascertain if a massively parallel hardware system can manage applications running within. Task execution happens dynamically and is controlled by the growth mechanism implementing efficient mapping in simulation. Algorithms that provide population simulation models are often inspired by those evidenced in biology and in particular those of cellular automata and L-systems. These algorithms are of particular interest due to their complexity and self-replication and recent research has shown that it is the refinement of the biological methodology that has resulted in their complexity. Further to this, adaptation of the design has moved the algorithm on towards being able to organize and build itself from a single cell. A growth model is utilized in software systems to provide production of meaningful data. The development of bio-inspired software is constrained by using contemporary processor architectures.
مقاله انگلیسی
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