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Time-Resolved Hanbury Brown–Twiss Interferometry of On-Chip Biphoton Frequency Combs Using Vernier Phase Modulation

Physical Review Applied 2023年第3期19卷 034019-034019页

作者： Karthik V. Myilswamy Suparna Seshadri Hsuan-Hao Lu Mohammed S. Alshaykh Junqiu Liu Tobias J. Kippenberg Andrew M. Weiner Joseph M. Lukens School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Quantum Information Science Section Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Electrical Engineering Department King Saud University Riyadh 11421 Saudi Arabia Institute of Physics Swiss Federal Institute of Technology Lausanne (EPFL) Lausanne 1015 Switzerland Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

Biphoton frequency combs (BFCs) are promising quantum sources for large-scale and high-dimensional quantum information and networking systems. In this context, the spectral purity of individual frequency bins will be critical for realizing quantum networking protocols like teleportation and entanglement swapping. Measurement of the temporal autocorrelation function of the unheralded signal or idler photons comprising the BFC is a key tool for characterizing their spectral purity and in turn verifying the utility of the biphoton state for networking protocols. Yet the experimentally obtainable precision for measuring BFC correlation functions is often severely limited by detector jitter. The fine temporal features in the correlation function—not only of practical value in quantum information, but also of fundamental interest in the study of quantum optics—are lost as a result. We propose a scheme to circumvent this challenge through electro-optic phase modulation, experimentally demonstrating time-resolved Hanbury Brown–Twiss characterization of BFCs generated from an integrated 40.5-GHz Si3N4 microring, up to a 3×3-dimensional two-qutrit Hilbert space. Through slight detuning of the electro-optic drive frequency from the comb’s free spectral range, our approach leverages Vernier principles to magnify temporal features, which would otherwise be averaged out by detector jitter. We demonstrate our approach under both continuous-wave and pulsed-pumping regimes, finding excellent agreement with theory. Our method reveals not only the collective statistics of the contributing frequency bins but also their temporal shapes—features lost in standard fully integrated autocorrelation measurements.

关键词： Quantum coherence & coherence measures Quantum communication Quantum correlations in quantum information Quantum entanglement Quantum information processing Quantum networks Quantum optics

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RETRACTED ARTICLE: A comparative recognition research on excretory organism in medical applications using neural networks

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Multimedia Tools and Applications 2023年 1-2页

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Sultanate of Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Chennai India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Abha Kingdom of Saudi Arabia

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Optimized Deformed Residual Neural Network Accompanied by a Featured Discriminator for Wet and Fine-Grained Fingerprint Sensor Image

SSRN

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SSRN 2024年

作者： Chen, Pin-Hsuan Chiu, Ching-Te Chen, Sung-Che Hsu, Mao-Hsiu Institute of Communications Engineering National Tsing Hua University 101 Section 2 Guangfu Road East Distric Hsinchu300 Taiwan Information Systems and Applications National Tsing Hua University 101 Section 2 Guangfu Road East Distric Hsinchu300 Taiwan Department of Computer Science National Tsing Hua University 101 Section 2 Guangfu Road East Distric Hsinchu300 Taiwan Nation Formosa University Electro-Optical Engineering No. 64 Wenhua Road Yunlin County Huwei Township632 Taiwan

Fingerprint recognition is crucial for device and data security, especially with the widespread use of capacitive sensors in mobile devices. However, denoising wet fingerprints from these sensors poses challenges due to small fingerprint areas, limited features, and significant moisture-induced dark regions. Our "DRB-FD" method combines a Featured Discriminator (FD) and a Deformed Residual Block (DRB) with attention mechanisms, drop-out layers, and pre-activation. In experiments using the Nasic9395 0606 aug dataset, DRB-FD achieved a remarkable 73.1% improvement, with the False Rejection Rate (FRR) decreasing by 25% compared to the baseline PGT-Net. Both FD and DRB significantly contributed, showing improvements of 47.4% (FRR decreased by 16.2%) and 48.9% (FRR decreased by 8.8%), respectively. Besides achieving better FRR results, there is a significant increase in the number of fingerprint points successfully matched after repair by the DRB-FD model. In conclusion, our innovative approach effectively denoises wet fingerprints from capacitive sensors in mobile systems, substantially enhancing the accuracy and reliability of fingerprint recognition systems. This advancement represents a significant step forward in biometric technology, offering promising applications in mobile security and user authentication. © 2024, The Authors. All rights reserved.

关键词： Capacitive sensors

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Numerical Analysis of Dielectrophoresis-Based Microfluidic Chip with a Facing-Electrode Design for Cell Separation

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Journal of Biosystems engineering 2024年第1期49卷 29-40页

作者： Nguyen, Thu Hang Nguyen, Mai Chi Nguyen, Hoang Trung Thi, Y Van Tran Vu, Ngoc Trung Thu, Hang Bui Duc, Trinh Chu Bui, Tung Thanh Jen, Chun-Ping Quang, Loc Do University of Engineering and Technology Vietnam National University 144th Xuan Thuy Cau Giay Hanoi Viet Nam University of Science Vietnam National University 334th Nguyen Trai Thanh Xuan Hanoi Viet Nam Hanoi University of Public Health 1A Duc Thang Road Duc Thang Ward Bac Tu Liem Hanoi Viet Nam Faculty of Mathematics and Computer Science Babes-Bolyai University Strada Mihail Kogălniceanu 1 Cluj-Napoca400347 Romania Department of Mechanical Engineering and Advanced Institute of Manufacturing for High-Tech Innovations National Chung Cheng University No.168 Section 1 University Road Minhsiung Chiayi621301 Taiwan School of Dentistry College of Dental Medicine Kaohsiung Medical University No.100 Shih-Chuan 1st Road Sanmin Kaohsiung80708 Taiwan

Purpose: Circulating tumor cell separation and analysis have played a critical role in cancer diagnosis, prognosis, and treatment. In this work, we aim to design and investigate a novel biochip that integrates dielectrophoresis, microfluidic technology to separate circulating tumor cells from blood cells. To create a dielectrophoresis-induced non-uniform electric field, a facing-electrode design was proposed and utilized, in which a slanted electrode array and a simple rectangular ground electrode are placed parallel on the top and bottom parts of the microfluidic channel, respectively. This design can reduce the particle position dependence in the microchannel and the complexity of the microfabrication process. Methods: The separation process, efficiency, and optimization of the proposed device were numerically investigated using the finite element method. Parametric research was conducted to comprehensively examine the impact of various operating and design factors on the cell movement and trajectories in the microfluidic device. Results: The results indicated the potential of the proposed biochip to ensure cancer cell separation from blood cells with high efficiency, high purity in a label-free, non-invasive, easy integration, and low-cost manner. Under the optimal conditions, the separation efficiency reached 92%, 88%, and 96% for human colon cancer cells (HT-29), red blood cells, and white blood cells, respectively. Conclusions: In this study, a novel DEP-based microfluidic chip was proposed to separate HT-29 tumor cells from blood cells and numerically investigated to verify the performance of the biochip design. Our findings could provide a foundation for further theoretical and practical investigations. The proposed system can separate cancer cells from red blood cells and white blood cells as well as offers numerous advantages, such as compact size, low voltage, high efficiency, non-invasiveness, and label-free nature. The tumor cell enrichment platform has

关键词： Microfluidic chips

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Classification of Breast Cancer using a Novel Neural Network-based Architecture

Classification of Breast Cancer using a Novel Neural Network...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Priti Mishra Sangita Khare Tripty Singh Rekha R Nair Mansi Sharma Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Cloud Computing SW Research Section Future Computing Research Division Artificial Intelligence Research Laboratory Electronics and Telecommunications Research Institute (ETRI) South Korea

Breast cancer is currently one of the deadliest types of cancer, and the death rate has considerably grown as a result of a lack of knowledge about the disease, its symptoms, and prevention techniques. Therefore, early detection at a nearly stage is crucial and vital in order to limit the progression of cancer. Malignant and benign breast cancer are the two further subtypes. In order to categorise the different forms of breast cancer, an automated system with logistic regression and neural network is proposed. The classification of the breast cancer data utilises the DNN with various levels of processing. The Digital Database for Screening Mammography (DDMS) dataset was used in the proposed study on the Kaggle platform. The dataset was divided into various train-test splits. On the basis of accuracy, ROC AUC cuve the system’s performance is evaluated. The outcome demonstrates that proposed-2 outperforms in a comparable sense with a training accuracy of 98.99% and testing accuracy of 98.83%.

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How to implement a decision support for digital health: Insights from design science perspective for action research in tuberculosis detection

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International Journal of Information Management Data Insights 2022年第2期2卷

作者： Odu, Nkiruka Bridget Prasad, Rajesh Onime, Clement Sharma, Birendra Kumar Computer Science Department African University of Science and Technology Abuja Nigeria Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Information and Communications Technology Section (ICTS) The Abdus Salam International Centre for Theoretical Physics Trieste Italy

Association rule mining has gained much popularity in facilitating disease diagnosis and the healthcare industry's decision-making process. The cases of Drug Resistance Tuberculosis (DR-TB) have substantially increased in recent times and the factors driving its rapid rise have not been clearly identified. This research explores the Frequent Pattern (FP) Growth algorithm to identify recurring relationships, disease co-occurrences, and generate some very interesting diagnostics rules for DR-TB. This is achieved through data extraction from patients' TB health database, transforming raw data into a knowledge discovery system that provides a good idea for further exploration through medical research to clarify the unknown patterns from the obtained result. The FP growth algorithm efficiently generates the frequent symptoms associated with pulmonary tuberculosis and drug-resistance tuberculosis by generating the association rules at minimum confidence of 80%. The analysis performed is knowledgeable and conforms to the clinical practices and design science research processes. © 2022 The Author(s)

关键词： Artificial intelligence Association rule mining Digital health Healthcare automation Machine learning

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Application of a novel generative adversarial network to wind power forecasting 8

Application of a novel generative adversarial network to win...

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8th International Conference of Precision Machinery and Manufacturing Technology, ICPMMT 2023

作者： Liao, G.C. Wu, R.C. Wu, T.T. Liao, B.T. Department of Electrical Engineering Lunghwa University of Science and Technology No. 300 Sec.1 Wanshou Rd. Guishan District Taoyuan City333326 Taiwan Department of Electrical Engineering I-Shou University No. 1 Sec. 1 Syuecheng Rd. Dashu District Kaohsiung City84001 Taiwan Department of Computer Science & Information Engineering Chang Gung University No. 259 Wenhua 1st Rd. Guishan Dist. Taoyuan City33302 Taiwan HQQA Johnson Health Tech. Co. Ltd. No. 999 Dongda Road Daya District Section 2 Taichung City428314 Taiwan

As the global economy rapidly develops, energy consumption and carbon dioxide emissions have increased annually, prompting countries to strive for carbon neutrality by 2050. Accurate wind power forecasting can aid power system dispatch departments to obtain wind farms' output and improve the power system's new energy absorption capacity by coordinating multiple power generation resources. To this end, this study proposes a novel method for wind power forecasting: the Generative Adversarial Network method-based Deep Q Neural Network (GDQN). Wind power is a nonlinear model with random characteristics like dynamics and uncertainty. The GDQN generates wind power data similar to historical wind power data, solving the problem of insufficient wind power data samples by developing adversarial networks. The deep Q-learning network is then utilized to predict future wind power data. The experimental results based on the actual test of the total power generated by all wind turbines in a complete wind farm indicate that the proposed GDQN method can significantly reduce the Mean Absolute Percentage Error (MAPE %) of wind power forecasting, as compared to other commonly used methods in wind power forecasting. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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Procrustean entanglement concentration in quantum-classical networking

arXiv

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arXiv 2024年

作者： Lu, Hsuan-Hao Alshowkan, Muneer Alnas, Jude Lukens, Joseph M. Peters, Nicholas A. Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States School of Electrical and Computer Engineering Duke University DurhamNC27708 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

The success of a future quantum internet will rest in part on the ability of quantum and classical signals to coexist in the same optical fiber infrastructure, a challenging endeavor given the orders of magnitude differences in flux of single-photon-level quantum fields and bright classical traffic. We theoretically describe and experimentally implement Procrustean entanglement concentration for polarization-entangled states contaminated with classical light, showing significant mitigation of crosstalk noise in dense wavelength-division multiplexing. Our approach leverages a pair of polarization-dependent loss emulators to attenuate highly polarized crosstalk that results from imperfect isolation of conventional signals copropagating on shared fiber links. We demonstrate our technique both on the tabletop and over a deployed quantum local area network, finding a substantial improvement of two-qubit entangled state fidelity from approximately 75% to over 92%. This local filtering technique could be used as a preliminary step to reduce asymmetric errors, potentially improving the overall efficiency when combined with more complex error mitigation techniques in future quantum repeater networks. © 2024, CC BY.

关键词： Crosstalk

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Accurate prediction of immunoglobulin proteins using machine learning model

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Informatics in Medicine Unlocked 2022年 29卷

作者： Ghulam, Ali Sikander, Rahu Ali, Farman Khan Swati, Zar Nawab Unar, Ahsanullah Talpur, Dhani Bux Computerization and Network Section Sindh Agriculture University Tandojam Pakistan School of Computer Science Xidian University Xi'an China School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Karakoram International University Gilgit Pakistan School of Life Science University of Science and Technology of China China Department of Computer Sceince and Software Engineering Jinnah University for Women Karachi Pakistan

Introduction: In recent years, researchers have become increasingly interested in immunoglobulins. The antibodies, also known as immunoglobulins, are serum proteins that bind to specific antigens produced by B cells. The complicated nature of the proteins has been emphasized for over 100 years of research on the structure and function of immunoglobulin. A major B-cell protein, which is utilized by the immune system to identify and neutralize foreign objects such as bacteria and viruses, is the Antibody (Ab), also known as immunoglobulin (Ig). Methods: This research focuses on the molecular mechanisms that allow these numerous and diverse roles. Therefore, it is very important to improve the accuracy of immunoglobulin classification by applying effective methods for disease research. We use the immunoglobulin features which reduced feature dimension were selected to extract immunoglobulin features, based on the BLOSUM vector score matrix. We've developed an ensemble learning method called Extreme Gradient Boosting (XGBoost). Results: We proposed learning method called Extreme Gradient Boosting (XGBoost) to predict immunoglobulins with 0.9727% accuracy with quintuple cross-validation of data used. In addition, the best method for identifying mixed 2D key features was used to BLOSUM62 to distinguish immunoglobulins with an ROC AUC predicted score of 0.9700%. The methods used in this paper are very accurate in predicting immunoglobulin levels and finding the key features. Conclusions: The aim of this study was to calculate appropriate concentrations of immunoglobulin and to identify important traits. In this investigation, the BLOSUM62 matrix was employed, which is a frequently utilized device for calculating the alignment of two distinct protein sequences. The value of the BLOSUM62 Matrix is determined through a large-scale analysis of observed polypeptide alignments. The best feature set created by BLOSUM62 matrix was found to reliably predict 0.9727% accuracy score o

关键词： Chi-squared automatic interaction detector Congenital hypothyroidism Iterative Dichotomiser-3 multilayer perceptron Newborn screening program Support vector machine

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A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

arXiv

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arXiv 2023年

作者： Alshahrani, Norah M. Kiah, M.L. Mat Zaidan, B.B. Alamoodi, A.H. Saif, Abdu Department of Computer System & Technology Faculty of Computer Science & IT University of Malaya Lembah Pantai Kuala Lumpur50603 Malaysia Department of Computer Science Applied Collage King Khalid University P.O. Box 62569 Abha Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges, and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users’ confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development. © 2023, CC BY.

关键词： Smart contract

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