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Controlling and measuring a superposition of position and momentum

Physical Review A 2023年第1期108卷 012215-012215页

作者： Takafumi Ono Nigam Samantarray John G. Rarity Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory and Department of Electrical & Electronic Engineering University of Bristol Bristol BS8 1FD United Kingdom Program in Advanced Materials Science Faculty of Engineering and Design Kagawa University 2217-20 Hayashi-cho Takamatsu Kagawa 761-0396 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Physics University of Strathclyde John Anderson Building 107 Rottenrow Glasgow G4 0NG United Kingdom

We present an observation of quantum interference between position and momentum with photons. We prepared a superposition of position and momentum states by using slits, lenses, and an interferometer. The precise control and measuring of propagation time of the photon results in a clear quantum interference of 85% visibility. The generated quantum state is then used to verify the violation of particle propagation inequality proposed by Hofmann [Phys. Rev. A 96, 020101(R) (2017)], resulting in 5.9% below the statistically predicted lower bound. We believe that this result is a major step towards further developments in quantum theory, such as fundamental modifications to the interpretation of quantum mechanics.

关键词： Quantum control Quantum information theory Quantum measurements Quantum metrology

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EEG dataset classification using CNN method 5

EEG dataset classification using CNN method

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5th International Conference on technology and Vocational Teachers, ICTVT 2019

作者： Mao, W.-L. Fathurrahman, H.I.K. Lee, Y. Chang, T.W. Department of Electrical Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu64002 Taiwan Bachelor Program in Interdisciplinary Studies College of Future National Yunlin University of Science and Technology Taiwan Department of Digital Media Design National Yunlin University of Science and Technology Taiwan

This paper proposes a simple Convolutional Neural Network (CNN) program to classify epileptic seizure. The diagnosis of epileptic seizure involves the identification and different characteristic of the Electroencephalography (EEG) signal. As such, it needs a method for identifying and classifying epileptic seizure. Deep learning is part of a neural network that has the ability and pattern to identify and classify epileptic seizure. CNN has been demonstrated high performance on image classification and pattern detection. In this paper, we combine the continuous wavelet transform (CWT) and CNN to classify epileptic seizure. This experiment uses the wavelet transform to convert signal data of EEG to time-frequency domain images. The output of the wavelet transform is an image that will classify into five attributes. In this experiment, we develop a simple program that will compare with other CNN approach (AlexNet and GoogleNet). The results of this experiment are two kinds of data, accuracy, and loss. The resulting accuracy is 72.49%, and the loss is 0.576. This result has a better learning time than GoogleNet and smaller loss result than AlexNet. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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A Proactive Approach of Optimizing Real-Time Equipment Monitoring Settings for Enhancing End-of-Line Yield

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Lin, Kuan-Chun Chang, Shi-Chung Liao, Yu-Chi Wu, Cheng-Wei National Taiwan University Electrical Engineering Department Taipei Taiwan Department of Process Integration Engineering TSMC WA United States TSMC lnc Power Semiconductor Technology Program Hsinchu Taiwan

In semiconductor manufacturing, wafer acceptance test (WAT) data consists of end-of-line (EOL) electrical parameters reflecting product quality and process capability, while in-line equipment plays a crucial role in shaping these outcomes. Engineers collect real-time monitoring (RTM) data that are used for reactive diagnosis when WAT detects issues. It is highly desirable to have quantitative prediction models linking RTM data to EOL parameters, so that RTM control region settings can be proactively optimized to keep WAT results on target with low variations, ultimately enhancing EOL yield. This paper designs WAPOR, a framework for EOL parameter prediction exploiting significant RTM items and their monitoring setting optimization, to proactively reduce resultant WAT variations. There are three innovations: (i) Key RTM Item Identification (H-RIS) for individual EOL parameters by combining three machine learning methods for both linear and non-linear analysis;(ii) WAT Parameter Prediction Model (WPBM) learned from applying Deep Back-Propagation Neural Networks (DBPN) to multi-dimensional, non-linear prediction of an EOL parameter value based on its key RTM items;and (iii) equipment monitoring control setting optimization (RRS-GA) to make WAT on target with low variation. As such, WAPOR moves beyond traditional linear approaches, uncovers complex relationships and empowers engineers to set RTM parameters proactively to make WAT forecast fall within WAT specification and minimize its variance. Simulation results demonstrate that WAPOR maintains WAT target alignment within 2% of the target while reducing variation by 49%. WAPOR has a good potential to improve process capability and EOL yield. © 1988-2012 IEEE.

关键词： Acceptance tests

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Fast Data-independent KLT Approximations Based on Integer Functions

arXiv

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arXiv 2024年

作者： Radünz, A.P. Coelho, D.F.G. Bayer, F.M. Cintra, R.J. Madanayake, A. Graduate Program in Statistics Universidade Federal de Pernambuco Recife Brazil Calgary Canada Departamento de Estatística and LACESM Universidade Federal de Santa Maria Santa Maria97105-900 Brazil Signal Processing Group Department of Technology Universidade Federal de Pernambuco Caruaru Brazil Department of Electrical and Computer Engineering Florida International University FL33174 United States

The Karhunen-Loève transform (KLT) stands as a well-established discrete transform, demonstrating optimal characteristics in data decorrelation and dimensionality reduction. Its ability to condense energy compression into a select few main components has rendered it instrumental in various applications within image compression frameworks. However, computing the KLT depends on the covariance matrix of the input data, which makes it difficult to develop fast algorithms for its implementation. Approximations for the KLT, utilizing specific rounding functions, have been introduced to reduce its computational complexity. Therefore, our paper introduces a category of low-complexity, data-independent KLT approximations, employing a range of round-off functions. The design methodology of the approximate transform is defined for any block-length N, but emphasis is given to transforms of N = 8 due to its wide use in image and video compression. The proposed transforms perform well when compared to the exact KLT and approximations considering classical performance measures. For particular scenarios, our proposed transforms demonstrated superior performance when compared to KLT approximations documented in the literature. We also developed fast algorithms for the proposed transforms, further reducing the arithmetic cost associated with their implementation. Evaluation of field programmable gate array (FPGA) hardware implementation metrics was conducted. Practical applications in image encoding showed the relevance of the proposed transforms. In fact, we showed that one of the proposed transforms outperformed the exact KLT given certain compression ratios. Copyright © 2024, The Authors. All rights reserved.

关键词： Image compression

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Demonstration of a Bosonic Quantum Classifier with Data Reuploading

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Physical Review Letters 2023年第1期131卷 013601-013601页

作者： Takafumi Ono Wojciech Roga Kentaro Wakui Mikio Fujiwara Shigehito Miki Hirotaka Terai Masahiro Takeoka Program in Advanced Materials Science Faculty of Engineering and Design Kagawa University 2217-20 Hayashi-cho Takamatsu Kagawa 761-0396 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Electronics and Electrical Engineering Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology (NICT) Koganei Tokyo 184-8795 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe 651-2492 Japan

In a single qubit system, a universal quantum classifier can be realized using the data reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrate it using a silicon-based photonic integrated circuit. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two photons resulted in a classification with a success probability of 94±0.8% in the proof of principle experiment. As this method can be applied to an arbitrary two-mode N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multiphoton states, is expected in the future.

关键词： Machine learning Optical quantum information processing Quantum algorithms

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Kalman Filter Design for Intermittent Optical Wireless Communication Systems on Time Scales

Kalman Filter Design for Intermittent Optical Wireless Commu...

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International Conference on Systems and Control (ICSC)

作者： Wenqi Cai Bacem Ben Nasser Mohamed Djemai Taous Meriem Laleg-Kirati Electrical and Computer Engineering Program King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia INSA Hauts-de-France LAMIH UMR CNRS 8201 UPHF QUARTZ Laboratory EA 7393 ENSEA INRIA Paris Saclay France

ISBN: (数字)9781665465076

ISBN: (纸本)9781665465083

Time-scale theory, due to its ability to unify the continuous and discrete cases, allows handling intractable non-uniform measurements, such as intermittent received signals. In this work, we address the state estimation problem of a vibration-induced intermittent optical wireless communication (OWC) system by designing a Kalman filter on time scales. First, the algorithm of the time-scale Kalman filter is introduced and a numerical example is given for illustration. Then the studied intermittent OWC system is presented, and experimental data are collected to determine the time scale's form, which has bounded graininess (a.k.a, bounded time jumps). Finally, we design a Kalman filter on the previously defined time scale for the intermittent OWC system and critically analyzed its estimation performance. Moreover, the obtained conclusions are further validated on a reference system. The simulation results corroborate that the time-scale Kalman filtering technique is considerably promising to solve the state estimation problem with non-uniform measurements. This study reveals for the first time the feasibility of applying the time-scale Kalman filter theory to practical applications.

关键词： Wireless communication Optical filters Integrated optics Optical design Filtering Simulation Current measurement

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Reduce Fingerprint Construction for Positioning IoT Devices Based on Generative Adversarial Nets 1

Reduce Fingerprint Construction for Positioning IoT Devices ...

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Tarekegn, Getaneh Berie Juang, Rong-Terng Lin, Hsin-Piao Munaye, Yirga Yayeh Belay Adege, Abebe National Taipei University of Technology Department of International Graduate Program in Electrical Engineering and Computer Science Taipei Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei Taiwan Debre Markos University Department of Information Technology Debre Markos Ethiopia

ISBN: (纸本)9781665404839

Fingerprint-based positioning is popular and applicable for Internet of Things (IoT) applications to offer seamless, intelligent and adaptive location-aware services for IoT devices. However, it takes time and cost to build the radio-map. This paper proposed deep convolutional Generative adversarial nets (DCGANs) to minimize the site survey time and cost, and to mitigate signal fluctuations. The radio-map was designed for receiving radio signals from detectable wireless local area network (WLAN) and cellular networks in scalable environments. The proposed fingerprinting-based positioning is a sequential combination of the hybrid support vector machine and long short-term memory algorithms. The experimental results indicate that the proposed method achieves a promising and reasonable positioning performance for IoT devices in scalable wireless environments. © 2020 IEEE.

关键词： Support vector machines

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Hyperbolic phonon-polariton electroluminescence in graphene-hBN van der Waals heterostructures

arXiv

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arXiv 2023年

作者： Guo, Qiushi Esin, Iliya Li, Cheng Chen, Chen Liu, Song Edgar, James H. Zhou, Selina Demler, Eugene Refael, Gil Xia, Fengnian Department of Electrical Engineering Yale University New Haven06511 United States Photonics Initiative Advanced Science Research Center City University of New York NY United States Physics Program Graduate Center City University of New York New YorkNY United States Department of Physics Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Tim Taylor Department of Chemical Engineering Kansas State University ManhattanKS66506 United States Department of Electrical Engineering California Institute of Technology PasadenaCA91125 United States Institute for Theoretical Physics ETH Zurich Zürich Switzerland

Phonon-polaritons are electromagnetic waves resulting from the coherent coupling of photons with optical phonons in polar dielectrics. Due to their exceptional ability to confine electric fields to deep subwavelength scales with low loss, they are uniquely poised to enable a suite of applications beyond the reach of conventional photonics, such as sub-diffraction imaging and near-field energy transfer. The conventional approach to exciting phonon-polaritons through optical methods, however, necessitates costly mid-infrared and terahertz coherent light sources along with near-field scanning probes, and generally leads to low excitation efficiency due to the substantial momentum mismatch between phonon-polaritons and free-space photons. Here, we demonstrate that under proper conditions, phonon-polaritons can be excited all-electrically by flowing charge carriers. Specifically, in hexagonal boron nitride (hBN)/graphene heterostructures, by electrically driving charge carriers in ultra-high-mobility graphene out of equilibrium, we observe bright electroluminescence of hBN’s hyperbolic phonon-polaritons (HPhPs) at mid-IR frequencies. The HPhP electroluminescence shows a temperature and carrier density dependence distinct from black-body or super-Planckian thermal emission. Moreover, the carrier density dependence of HPhP electroluminescence spectra reveals that HPhP electroluminescence can arise from both inter-band transition and intra-band Cherenkov radiation of charge carriers in graphene. The HPhP electroluminescence offers fundamentally new avenues for realizing electrically-pumped, tunable mid-IR and THz phonon-polariton lasers, and efficient cooling of electronic devices. © 2023, CC BY.

关键词： Photons

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