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ردیف | عنوان | نوع |
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1 |
Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105 The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database
systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM)
systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and
control systems are operated by different units at NYCDOT as an independent database or operation system. New York City
experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial
systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all
users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to
develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City.
This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS,
involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and
wireless communication features for data collection and reduction; 2) interface development among existing database and control
systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study
paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from
loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi
and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day.
GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept.
© 2014 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshu
Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data |
مقاله انگلیسی |
2 |
Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022 Contact tracing has been proven an essential practice during pandemic outbreaks and is a
critical non-pharmaceutical intervention to reduce mortality rates. While traditional con-
tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based
systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is
steadily growing in smart city environments. This work presents a contact tracing frame-
work that logs smart space users’ co-existence using IoT devices as reference anchors. The
design is non-intrusive as it relies on passive wireless interactions between each user’s
carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti-
lizing received signal strength indicators (RSSI). The proposed framework can log the iden-
tities for the interacting pair, their estimated distance, and the overlapping time duration.
Also, we propose a machine learning-based infection risk classification method to char-
acterize each interaction that relies on RSSI-based attributes and contact details. Finally,
the proposed contact tracing framework’s performance is evaluated through a real-world
case study of actual wireless interactions between users and IoT devices through Bluetooth
Low Energy advertising. The results demonstrate the system’s capability to accurately cap-
ture contact between mobile users and assess their infection risk provided adequate model
training over time.
© 2021 Elsevier B.V. All rights reserved. keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification |
مقاله انگلیسی |
3 |
Development of an Undergraduate Quantum Engineering Degree
توسعه یک مدرک کارشناسی مهندسی کوانتوم-2022 Quantum computing, communications, sensing, and simulations are radically transformative
technologies, with great potential to impact industries and economies. Worldwide, national governments,
industries, and universities are moving to create a new class of workforce—the Quantum Engineers. Demand
for such engineers is predicted to be in the tens of thousands within a five-year timescale, far exceeding the
rate at which the world’s universities can produce Ph.D. graduates in the discipline. How best to train this
next generation of engineers is currently a matter of debate. Quantum mechanics—long a pillar of traditional
physics undergraduate degrees—must now be merged with traditional engineering offerings. This article
discusses the history, development, and the first year of operation of the world’s first undergraduate degree
in quantum engineering to be grown out of an engineering curriculum. The main purpose of this article is
to inform the wider discussion, now being held by many institutions worldwide, on how best to formally
educate the Quantum Engineer.
INDEX TERMS: Degree | education | engineering | quantum | undergraduate. |
مقاله انگلیسی |
4 |
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 |
5 |
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 |
6 |
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 |
7 |
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 |
8 |
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 |
9 |
A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
بررسی حملات خصمانه در بینایی کامپیوتر: طبقه بندی، تجسم و جهت گیری های آینده-2022 Deep learning has been widely applied in various fields such as computer vision, natural language pro-
cessing, and data mining. Although deep learning has achieved significant success in solving complex
problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, result-
ing in models that fail to perform their tasks properly, which limits the application of deep learning
in security-critical areas. In this paper, we first review some of the classical and latest representative
adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowl-
edge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze
the subject development in this field based on the information of 5923 articles from Scopus. In the
end, possible research directions for the development about adversarial attacks are proposed based on
the trends deduced by keywords detection analysis. All the data used for visualization are available at:
https://github.com/NanyunLengmu/Adversarial- Attack- Visualization . keywords: یادگیری عمیق | حمله خصمانه | حمله جعبه سیاه | حمله به جعبه سفید | نیرومندی | تجزیه و تحلیل تجسم | Deep learning | Adversarial attack | Black-box attack | White-box attack | Robustness | Visualization analysis |
مقاله انگلیسی |
10 |
Equivalence Checking of Quantum Circuits With the ZX-Calculus
بررسی هم ارزی مدارهای کوانتومی با ZX-calculus-2022 As state-of-the-art quantum computers are capable of running increasingly complex algorithms, the need for
automated methods to design and test potential applications
rises. Equivalence checking of quantum circuits is an important,
yet hardly automated, task in the development of the quantum
software stack. Recently, new methods have been proposed that
tackle this problem from widely different perspectives. One of
them is based on the ZX-calculus, a graphical rewriting system
for quantum computing. However, the power and capability of
this equivalence checking method has barely been explored.
The aim of this work is to evaluate the ZX-calculus as a
tool for equivalence checking of quantum circuits. To this end,
it is demonstrated how the ZX-calculus based approach for
equivalence checking can be expanded in order to verify the
results of compilation flows and optimizations on quantum
circuits. It is also shown that the ZX-calculus based method
is not complete—especially for quantum circuits with ancillary
qubits. In order to properly evaluate the proposed method,
we conduct a detailed case study by comparing it to two other
state-of-the-art methods for equivalence checking: one based
on path-sums and another based on decision diagrams. The
proposed methods have been integrated into the publicly available
QCEC tool (https://github.com/cda-tum/qcec) which is
part of the Munich Quantum Toolkit (MQT).
Index Terms: Quantum computing | formal verification | quantum circuit. |
مقاله انگلیسی |