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ردیف | عنوان | نوع |
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1 |
Dual-Frequency Quantum Phase Estimation Mitigates the Spectral Leakage of Quantum Algorithms
تخمین فاز کوانتومی دو فرکانس برای کاهش نشت طیفی الگوریتمهای کوانتومی-2022 Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from
spectral leakage, when the reciprocal of the record length is not an
integer multiple of the unknown phase, which incurs an accuracy
degradation. For the existing single-sample estimation scheme,
window-based methods have been proposed for spectral leakage
mitigation. As a further advance, we propose a dual-frequency estimator, which asymptotically approaches the Cramér-Rao bound,
when multiple samples are available. Numerical results show that
the proposed estimator outperforms the existing window-based
methods, when the number of samples is sufficiently high.
Index Terms: Algorithmic error mitigation | dual-frequency estimator | quantum algorithms | quantum phase estimation. |
مقاله انگلیسی |
2 |
Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data
کاهش ابعاد کوانتومی برای تشخیص الگو در داده های فضایی-طیفی با وضوح بالا-2022 The promises of advanced quantum computing technology have driven research in the simulation of quantum computers on
classical hardware, where the feasibility of quantum algorithms for real-world problems can be investigated. In domains such as High
Energy Physics (HEP) and Remote Sensing Hyperspectral Imagery, classical computing systems are held back by enormous readouts
of high-resolution data. Due to the multi-dimensionality of the readout data, processing and performing pattern recognition operations
for this enormous data are both computationally intensive and time-consuming. In this article, we propose a methodology that utilizes
Quantum Haar Transform (QHT) and a modified Grover’s search algorithm for time-efficient dimension reduction and dynamic pattern
recognition in data sets that are characterized by high spatial resolution and high dimensionality. QHT is performed on the data to
reduce its dimensionality at preserved spatial locality, while the modified Grover’s search algorithm is used to search for dynamically
changing multiple patterns in the reduced data set. By performing search operations on the reduced data set, processing overheads
are minimized. Moreover, quantum techniques produce results in less time than classical dimension reduction and search methods.
The feasibility of the proposed methodology is verified by emulating the quantum algorithms on classical hardware based on field
programmable gate arrays (FPGAs). We present designs of the quantum circuits for multi-dimensional QHT and multi-pattern Grover’s
search. We also present two emulation techniques and the corresponding hardware architectures for this methodology. A high
performance reconfigurable computer (HPRC) was used for the experimental evaluation, and high-resolution images were used as the
input data set. Analysis of the methods and implications of the experimental results are discussed.
Index Terms— Quantum computing | field-programmable gate arrays (FPGAs) |
مقاله انگلیسی |
3 |
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 |
4 |
An R-Convolution Graph Kernel Based on Fast Discrete-Time Quantum Walk
یک هسته گراف R-Convolution بر اساس راه رفتن کوانتومی سریع زمان گسسته -2022 In this article, a novel R-convolution kernel,
named the fast quantum walk kernel (FQWK), is proposed
for unattributed graphs. In FQWK, the similarity of the
neighborhood-pair substructure between two nodes is measured
via the superposition amplitude of quantum walks between
those nodes. The quantum interference in this kind of local
substructures provides more information on the substructures so
that FQWK can capture finer-grained local structural features
of graphs. In addition, to efficiently compute the transition
amplitudes of multistep discrete-time quantum walks, a fast
recursive method is designed. Thus, compared with all the
existing kernels based on the quantum walk, FQWK has the
highest computation speed. Extensive experiments demonstrate
that FQWK outperforms state-of-the-art graph kernels in terms
of classification accuracy for unattributed graphs. Meanwhile,
it can be applied to distinguish a larger family of graphs, including cospectral graphs, regular graphs, and even strong regular
graphs, which are not distinguishable by classical walk-based
methods.
Index Terms: Discrete-time quantum walk (DTQW) | graph classification | graph kernel | R-convolution kernel. |
مقاله انگلیسی |
5 |
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021 Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding. Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception |
مقاله انگلیسی |
6 |
Computer vision based food grain classification: A comprehensive survey
طبقه بندی دانه های غذایی مبتنی بر بینایی رایانه ای: یک مرور جامع-2021 This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches pro- posed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented. Keywords: Computer vision approaches | Quality inspection | Food grain identification | Machine vision |
مقاله انگلیسی |
7 |
Online detection of naturally DON contaminated wheat grains from China using Vis-NIR spectroscopy and computer vision
تشخیص آنلاین دانه های گندم آلوده به DON طبیعی از چین با استفاده از طیف سنجی Vis-NIR و بینایی ماشین-2021 Deoxynivalenol (DON) contamination of wheat grains is a serious problem in China, and it
is necessary to remove contaminated wheat before it enters the consumer market. In this
study, visible-near infrared (Vis-NIR) spectroscopy and computer vision techniques were
combined to simulate online discrimination between normal and DON-contaminated
wheat grains. Naturally growing wheat samples were collected from several of the main
wheat-producing areas in China, the reference DON contents were measured by using
liquid chromatography serial triple quadrupole mass spectrometer (LC-MS), and then
wheat samples were divided into two categories according to the national standard of
1 mg kg1. The characteristic spectral variables, colour and texture features were extracted
and integrated for chemometric analysis. Principal component analysis based on fusion
features indicated better clustering than with just spectral features. Subsequently, linear
discriminant analysis modelling based on spectra and texture features achieved the best
discrimination with an accuracy of 95.06% and 91.36% for calibration and validation sets
respectively, which was 5% higher than with just spectral features, and the false positive
rates (FPR) were the lowest: 3.41% and 10.42% for calibration and validation sets respectively. The internal scanning results of whole wheat flour indicated that the higher the
content of DON, the looser the binding of starch granules, which could cause the textural
change of wheat grains. The research showed that Vis-NIR spectroscopy combined with
computer vision has the potential to be used in the non-destructive and online detection of
DON-contaminated wheat grains; further study on the interference from complex environments is still need for actual online detection.
Keywords: Vis-NIR spectroscopy | Computer vision | Wheat grains | DON | Features fusion |
مقاله انگلیسی |
8 |
تمایز خونریزی حاصل از افزایش کنتراست با استفاده از CT طیف سنجی با لایه دوگانه در بیماران سکته حاد
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 8 بیماران سکته حاد به مراکز دارای ترومبکتومی(لخته برداری) (TCC) انتقال می یابند و پس از ورود به TCC ، تحت معاینه CT سر قرار می گیرند تا نمره ASPECTS و خونریزی داخل جمجمه آنها ارزیابی شود. تقویت پارانشیمی در بیمارانی که کنتراست یددار یا iodinated را قبل از انتقال دریافت کرده اند، در شبیه سازی خونریزی در این CT و پس از انتقال کمک می کند. دو مورد از کاربرد تصویربرداری طیفی CT به منظور تمایز بین افزایش کنتراست پارانشیمی و خونریزی در این مطالعه گزارش شده است. روش TCC، تصویربرداری با انرژی دوگانه یا لایه دوگانه (طیفی) را برای این گروه بیمار در نظر می گیرد.
کلمات کلیدی: سکته مغزی | تصویربرداری طیفی CT |
مقاله ترجمه شده |
9 |
Plant trait estimation and classification studies in plant phenotyping using machine vision – A review
برآورد و طبقه بندی صفات گیاهی در فنوتیپ سازی گیاهان با استفاده از بینایی ماشین - مرور-2021 Today there is a rapid development taking place in phenotyping of plants using
non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for
thousands of plants in the field. Plant phenotyping systems either use single imaging
method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high
resolution volumetric imaging. This paper provides an overview of imaging techniques
and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.
In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future
research directions related to the use of deep learning based machine vision algorithms
for structural (2-D and 3-D), physiological and temporal trait estimation, and classification
studies in plants. Keywords: Plant phenotyping | Machine vision | Plant trait estimation | Imaging techniques | Leaf segmentation and counting | Plant classification studies |
مقاله انگلیسی |
10 |
Application of spectral features for separating homochromatic foreign matter from mixed congee
کاربرد ویژگیهای طیفی برای جداسازی مواد خارجی هم رنگ از مخروط مخروطی-2021 Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee. Keyword: Mixed congee | Homochromatic foreign matter | Hyperspectral imaging technology | Pattern recognition | Chemometrics |
مقاله انگلیسی |