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IEEE Photonics Conference (IPC)

作者： Hsuan-Hao Lu Muneer Alshowkan Jude Alnas Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee USA School of Electrical and Computer Engineering Duke University Durham North Carolina USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona USA

We employ polarization-dependent loss emulators for Procrustean filtering of a noisy quantum state affected by classical crosstalk, resulting in a substantial improvement of two-qubit entangled state fidelities from a...

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Optimizing Variable-Strength Combinatorial Test Suite Generation via Fuzzy Adaptive Sine Cosine Algorithm (FASCA)

Optimizing Variable-Strength Combinatorial Test Suite Genera...

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Cyber Security and Computing (CyberComp), International Conference on

作者： Nur Syabila Zabidi Noraini Ibrahim Mazidah Mat Rejab MasrulEhsan Mamat Sumaira Nazir Nurul Hawani Mat Tuselim Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia (UTHM) Parit Raja Batu Pahat MALAYSIA Department of Software Engineering National University of Modern Languages Islamabad PAKISTAN Business Intelligence Section Industrial Research Management Centre SIRIM Berhad Shah Alam MALAYSIA

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

Combinatorial test suite generation is a critical aspect of software testing, particularly for systems with variable-strength interactions. Traditional optimization algorithms often struggle to efficiently generate minimal test suites while maintaining coverage. This research introduces the Fuzzy Adaptive Sine Cosine Algorithm (FASCA), a variant of the Sine Cosine Algorithm (SCA), tailored specifically for optimizing variable-strength combinatorial test suite generation. FASCA incorporates fuzzy adaptation mechanisms to dynamically balance exploration and exploitation, addressing the limitations of standard SCA in handling diverse interaction strengths. Experimental evaluations demonstrate that FASCA significantly reduces test suite size and execution time compared to the original SCA, offering a more efficient solution for combinatorial test optimization. These results highlight FASCA's potential as a robust tool for enhancing the efficiency of software testing processes.

关键词： Fuzzy logic Heuristic algorithms Software algorithms Metaheuristics Switches Minimax techniques Linguistics Inference algorithms Standards Gravity

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EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification

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Heliyon 2024年第23期10卷 e40820页

作者： Khan, Zeshan Aslam Waqar, Muhammad Cheema, Khalid Mehmood Bakar Mahmood, Ali Abu Ain, Quratul Chaudhary, Naveed Ishtiaq Alshehri, Abdullah Alshamrani, Sultan S. Zahoor Raja, Muhammad Asif International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin ROC Douliou 64002 Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi Pakistan Department of Electrical and Computer Engineering International Islamic University Islamabad 44000 Pakistan Department of Business Management Global Banking School London United Kingdom Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Yunlin ROC Douliu 64002 Taiwan Department of Information Technology Faculty of Computing and Information Al-Baha University Alaqiq 65779-7738 Saudi Arabia Department of Information Technology College of Computer and Information Technology Taif University Taif 21944 Saudi Arabia

The quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape. To improve the accuracy of models for classifying fruits and vegetables, researchers have introduced various CNN architectures like VGG16, AlexNet, DenseNet-121 and different attention-based variants by compromising models' complexity leading to high computational cost and overlooking feature's contribution for interpretable predictions. Additionally, the major drawback in most of the existing works is the utilization of the limited dataset and number of classes for fruit and vegetable classification task instead of the benchmark dataset like Fruit-360 which comprises of 141 distinct fruit and vegetable classes. In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. The proposed EA-CNN model (a) detects the class of fruits and vegetable accurately through visual features by manipulating undiscover customized pooling technique and enhanced attention feature extraction mechanism, (b) classifies the fruits and vegetables efficiently through a simplified architecture, (c) provides interpretable predictions through XAI approach. By utilizing entire fruit-360 database, this research noteworthily enhances the model's capability to classify wide range of fruits and vegetables, thus providing an effective and reliable solution for practical applications. The proposed study outclasses baseline models with regard to accuracy and computational cost on fruit-360 benchmark dataset. EA-CNN achieves better accuracy of 98.1 % in smaller number of iterations as compared to benchmark models. Moreover, to further prove the efficiency, generalization ability, robustness, scalability and adaptabilit

关键词： Classification Convolutional neural networks Deep learning Explainable AI

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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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Foundry-fabricated silicon source of broadband polarization entanglement

Foundry-fabricated silicon source of broadband polarization ...

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IEEE Photonics Conference (IPC)

作者： Alexander Miloshevsky Lucas M. Cohen Karthik V. Myilswamy Saleha Fatema Muneer Alshowkan Hsuan-Hao Lu Andrew M. Weiner Joseph M. Lukens Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona USA

We present a CMOS-fabricated photonic integrated circuit tailored to the generation of ultrabroadband polarization-entangled photons. This fully on-chip source leverages a novel design combining polarization splitter-rotators and a bidirectionally pumped microring resonator to produce high-fidelity polarization Bell states over 115 frequency-bin pairs spaced 38.4 GHz apart.

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Development of Localized Cutaneous Force Feedback System for Robotics Assisted Surgery Systems

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Procedia computer Science 2024年 246卷 1160-1169页

作者： Aaron Raymond See Thad Jacob Tiong Lanz Benedict De Guzman Kenneth Contee Garfield Greg Lim Carl Sebastian Yebes Department of Electrical Engineering Southern Taiwan University of Science and Technology No. 1 Nantai St Yongkang District Tainan City 71005 Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Section 2 Zhongshan Rd Taiping District Taichung City 411030 Taiwan Department of Electrical Engineering Universitas Katolik Indonesia Atma Jaya Jl. Jend. Sudirman No.51 5 RT.004/RW.4 Karet Semanggi Kecamatan Setiabudi Kota Jakarta Selatan Daerah Khusus Jakarta 12930 Indonesia School of Computer Studies University of San Jose - Recoletos Basak Pardo Cebu City 6000 Philippines College of Computer Studies Cebu Institute of Technology - University Natalio B. Bacalso Ave Cebu City 6000 Cebu Philippines

This study explores integrating a novel cutaneous force feedback system for robotic-assisted surgery (RAS) systems aimed at improving tactile perception for surgical precision and safety. The system developed comprises two major components: a force-sensitive matrix sensor and a 4x4 tactile pin interface. Forces experienced by the sensors are mirrored through the tactile interface, conveying the sensation of knowing which part of the sensor is in contact and how much force is subjected to it. An algorithm called Dynamic Interval Localization (DIL) was developed to manipulate pin actuation by scheduling individualized dynamic oscillation. The system successfully detected operating forces from 0.1N to 5N while concurrently providing respective cutaneous feedback. The system’s performance in force localization suggests significant potential and promises in elevating the haptic features of RAS systems.

关键词： robotic-assisted Surgery (RAS) haptic feedback force localization force matrix sensor dynamic interval localiztion

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A SURVEY ON COMPUTATIONAL SOLUTIONS FOR RECONSTRUCTING COMPLETE OBJECTS BY REASSEMBLING THEIR FRACTURED PARTS

arXiv

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arXiv 2024年

作者： Lu, Jiaxin Liang, Yongqing Han, Huijun Hua, Jiacheng Jiang, Junfeng Li, Xin Huang, Qixing Computer Science Department The University of Texas at Austin 2317 Speedway AustinTX United States Department of Computer Science and Engineering Texas A&M University 435 Nagle St College StationTX United States Department of Computer Science and Technology Tsinghua University 30 Shuangqing Road Beijing China College of Artificial Intelligence and Automation Hohai University 1915 Hohai Avenue Jiangsu Changzhou China Section of Visual Computing & Computational Media Department of Computer Science and Engineering Texas A&M University 789 Ross Street College StationTX United States

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Tomography of ultrabroadband polarization-frequency hyperentangled photons

Tomography of ultrabroadband polarization-frequency hyperent...

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IEEE Photonics Conference (IPC)

作者： Hsuan-Hao Lu Muneer Alshowkan Karthik V. Myilswamy Andrew M. Weiner Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Oak Ridge National Laboratory Oak Ridge Tennessee USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona USA

We generate photon pairs entangled in both frequency and polarization spanning the telecom C- and L-bands, characterize high-fidelity states in multiple wavelength channels, and show full tomography in 16- and 32-dime...

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Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

arXiv

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arXiv 2022年

作者： van Hulst, Jilles Poot, Maurice Kostić, Dragan Yan, Kai Wa Portegies, Jim Oomen, Tom Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology Netherlands Center of Competency ASM Pacific Technology Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input. © 2022, CC BY-NC-ND.

关键词： Feedforward control

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A feedforward artificial neural network model for classification and detection of type 2 diabetes 1

A feedforward artificial neural network model for classifica...

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1st International Conference on Recent Trends in Applied Research, ICoRTAR 2020

作者： Frimpong, Enoch A. Oluwasanmi, Ariyo Baagyere, Edward Y. Zhiguang, Qin School of Information and Software Engineering University of Electronic Science and Technology of China No.4 North Jianshe Road Section 2 Chengdu Chengdu610054 China Department of Computer Science University for Development Studies Box TL 1350 Tamale Ghana

Efforts to enhance accuracy in medical diagnostics in molecular medicine have contributed to the wide use of artificial neural network (ANN) algorithms for disease detection due to its ability to process large medical datasets and integrate them into characterized outputs to avoid misdiagnosis. Typically, the application of ANNs have proven useful in sample analyses of patients with diabetes and in decision support systems. Over the years, various ANN models have been utilized in medical diagnostics;however, these approaches still maintain certain levels of error and have lesser training and testing accuracies in disease detection. In this study, we propose a Feedforward Artificial Neural Network (FFANN) model with a dense neural network architecture suitable for processing numeric and textual dataset. We carefully designed our model structure to have the ability to maximize the number of layers and nodes required to learn every feature of the dataset and also to perform effective computations but avoiding model under fitting and overfitting which occurs when less or more layers are used respectively. This approach puts our model ahead of other state-of-the-art prediction models which have been proposed in terms of performance as it achieved 97.27% and 96.09% training and testing accuracies, respectively, for type 2 diabetes detection on Pima Indian Diabetes dataset. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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