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ردیف | عنوان | نوع |
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1 |
General Mixed-State Quantum Data Compression With and Without Entanglement Assistance
فشرده سازی داده های کوانتومی حالت مخلوط عمومی با و بدون کمک درهم تنیدگی-2022 We consider the most general finite-dimensional
quantum mechanical information source, which is given by a
quantum system A that is correlated with a reference system R.
The task is to compress A in such a way as to reproduce the
joint source state ρAR at the decoder with asymptotically high
fidelity. This includes Schumacher’s original quantum source
coding problem of a pure state ensemble and that of a single
pure entangled state, as well as general mixed state ensembles.
Here, we determine the optimal compression rate (in qubits per
source system) in terms of the Koashi-Imoto decomposition of
the source into a classical, a quantum, and a redundant part.
The same decomposition yields the optimal rate in the presence
of unlimited entanglement between compressor and decoder, and
indeed the full region of feasible qubit-ebit rate pairs.
keywords: Quantum information | source coding | entanglement. |
مقاله انگلیسی |
2 |
Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022 Computer vision-based techniques for structural displacement measurement are rapidly becoming
popular in civil structural engineering. However, most existing computer vision-based displace-
ment measurement methods require man-made targets for object matching or tracking, besides
usually the measurement accuracies are seriously sensitive to the ambient illumination variations.
A computer vision-based illumination robust and multi-point simultaneous measuring method is
proposed for structural displacement measurements. The method consists of two part, one is for
segmenting the beam body from its background, the segmentation is perfectly carried out by fully
convolutional network (FCN) and conditional random field (CRF); another is digital image cor-
relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory.
The accuracy and illumination robustness are verified through three groups of elaborately
designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation,
numbers of locations along with the segmented beam body can be chosen and measured simul-
taneously. It is verified that the method is illumination robust since the displacement measure-
ments are with the smallest fluctuations to the illumination variations. The proposed method does
not require any man-made targets attached on the structure, but because of the exploitation of
DIC in displacement measurement, the regions centered on the measuring points need to have
texture feature. keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation |
مقاله انگلیسی |
3 |
Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022 Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The
automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has
not been investigated previously. This determination will release workers from heavy workloads. This study
proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer
vision and geometric imaging theory. This novel method contained three integrated modules. The first module
used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to
detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and
mark points using the transformation of the five detected keypoints and several image processing technologies
such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module,
according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using
the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average
relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening
angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some
segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring
instruments, and advanced transformation methods can be applied to further improve detection accuracy. keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging |
مقاله انگلیسی |
4 |
Automated bridge surface crack detection and segmentation using computer vision-based deep learning model
تشخیص و تقسیم خودکار ترک سطح پل با استفاده از مدل یادگیری عمیق مبتنی بر بینایی کامپیوتری-2022 Bridge maintenance will become a widespread trend in the engineering industry as the number of bridges
grows and time passes. Cracking is a common problem in bridges with concrete structures. Allowing it to
expand will result in significant economic losses and accident risks This paper proposed an automatic detection
and segmentation method of bridge surface cracks based on computer vision deep learning models. First, a
bridge surface crack detection and segmentation dataset was established. Then, according to the characteristics
of the bridge, we improved the You Only Look Once (YOLO) algorithm for bridge surface crack detection.
The improved algorithm was defined as CR-YOLO, which can identify cracks and their approximate locations
from multi-object images. Subsequently, the PSPNet algorithm was improved to segment the bridge cracks
from the non-crack regions to avoid the visual interference of the detection algorithm. Finally, we deployed
the proposed bridge crack detection and segmentation algorithm in an edge device. The experimental results
show that our method outperforms other baseline methods in generic evaluation metrics and has advantages
in Model Size(MS) and Frame Per Second (FPS).
keywords: Bridge crack Crack detection | Crack segmentation | Deep learning | Computer vision |
مقاله انگلیسی |
5 |
A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method
یک رویکرد تشخیص و پیشبینی حریق هوشمند ترکیبی در زمان واقعی از طریق دوربینها بر اساس روش بینایی کامپیوتری-2022 Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very
limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire
circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire
detection and forecasting approach through cameras is discussed with extracting and predicting fire development
characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to charac-
terize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network
(RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire
forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accu-
racies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error
(MRE) of extraction by RCNN for the three parameters are around 4–13%, 6–20% and 11–37%, respectively.
Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4–13%, 11–33% and 12–48%,
respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire devel-
opment and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of
accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics. keywords: ایمنی آتش سوزی صنعتی | تشخیص حریق | پیش بینی آتش سوزی | تجزیه و تحلیل آتش سوزی | هوش مصنوعی | Industrial fire safety | Fire detection | Fire forecasting | Fire analysis | Artificial intelligence |
مقاله انگلیسی |
6 |
Co-segmentation inspired attention module for video-based computer vision tasks
ماژول توجه الهام گرفته از تقسیم بندی مشترک برای وظایف بینایی کامپیوتری مبتنی بر ویدئو-2022 Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between
those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing
pre-trained models to perform object detection, object segmentation and/or object pose estimation. Although
using pre-trained models is a viable approach, it has several limitations in the need for an exhaustive annotation
of object categories, a possible domain gap between datasets and a bias that is typically present in pre-trained
models. In this work, we propose to utilize the common rationale that a sequence of video frames capture a
set of common objects and interactions between them, thus a notion of co-segmentation between the video
frame features may equip the model with the ability to automatically focus on task-specific salient regions
and improve the underlying task’s performance in an end-to-end manner. In this regard, we propose a generic
module called ‘‘Co-Segmentation inspired Attention Module’’ (COSAM) that can be plugged in to any CNN
model to promote the notion of co-segmentation based attention among a sequence of video frame features.
We show the application of COSAM in three video-based tasks namely: (1) Video-based person re-ID, (2) Video
captioning, & (3) Video action classification and demonstrate that COSAM is able to capture the task-specific
salient regions in video frames, thus leading to notable performance improvements along with interpretable
attention maps for a variety of video-based vision tasks, with possible application to other video-based vision
tasks as well.
keywords: توجه | تقسیم بندی مشترک | شناسه شخص | زیرنویس ویدیویی | طبقه بندی ویدیویی | Attention | Co-segmentation | Personre-ID | Video-captioning | Video classification |
مقاله انگلیسی |
7 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
8 |
Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022 Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used
during their storage, transportation purpose. These are responsible factors that decide the freshness of a post
harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the
freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre-
lation has been observed between the colour of the eye and different duration of storage day. It is segmented
from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory
features. These extracted features show a degradation pattern which acts as an indicative parameter to determine
the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The
experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish
freshness. The high accuracy of freshness detection and low computation time makes this non-destructive
methodology efficient for real-world usage in the fish industry and market. keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation |
مقاله انگلیسی |
9 |
Plasmonic Waveguides: Enhancing quantum electrodynamic phenomena at nanoscale
موجبرهای پلاسمونیک: افزایش پدیده های الکترودینامیکی کوانتومی در مقیاس نانو-2022 The emerging field of plasmonics may lead to enhanced light–matter interactions at extremely nanoscale regions. Plasmonic (metallic) devices promise to effi- ciently control classical and quantum properties of light.
Plasmonic waveguides are usually employed to excite confined electromagnetic modes at nanoscale that can strongly
interact with matter. Analysis shows that nanowaveguides
share similarities with their low-frequency microwave counterparts. In this article, we review ways to study plasmonic
nanostructures coupled to quantum optical emitters from a
classical electromagnetic perspective. Quantum emitters are
mainly used to generate single-photon quantum light that
can be employed as a quantum bit, or “qubit,” in envisioned
quantum information technologies. We demonstrate different
ways to enhance a diverse range of quantum electrodynamic
phenomena based on plasmonic configurations by using the
Green’s function formalism, a classical dyadic tensor. More
specifically, spontaneous emission and superradiance are
analyzed through Green’s function-based field quantization.
The exciting new field of quantum plasmonics could lead to
a plethora of novel optical devices for communications and
computing applications in the quantum realm, such as efficient single-photon sources, quantum sensors, and compact
on-chip nanophotonic circuits.
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مقاله انگلیسی |
10 |
Random Telegraph Noise of a 28-nm Cryogenic MOSFET in the Coulomb Blockade Regime
نویز تصادفی تلگراف یک ماسفت برودتی 28 نانومتری در رژیم بلوک کولن-2022 We observe rich phenomena of two-level random telegraph noise (RTN) from a commercial bulk 28-nm
p-MOSFET (PMOS) near threshold at 14 K, where a Coulomb
blockade (CB) hump arises from a quantum dot (QD) formed
in the channel. Minimum RTN is observed at the CB hump
where the high-current RTN level dramatically switches to
the low-current level. The gate-voltage dependence of the
RTN amplitude and power spectral density match well with
the transconductance from the DC transfer curve in the CB
hump region. Our work unequivocally captures these QD
transport signatures in both current and noise, revealing
quantum confinement effects in commercial short-channel
PMOS even at 14 K, over 100 times higher than the typical dilution refrigerator temperatures of QD experiments
(<100 mK). We envision that our reported RTN characteristics rooted from the QD and a defect trap would be
more prominent for smaller technology nodes, where the
quantum effect should be carefully examined in cryogenic
CMOS circuit designs.
Index Terms: 28-nm CMOS | cryogenic CMOS | random telegraph noise | quantum dot | Coulomb blockade. |
مقاله انگلیسی |