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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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A novel medical image enhancement technique based on hybrid method

A novel medical image enhancement technique based on hybrid ...

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International Laser technology and Optics Symposium 2022, iLATOS 2022

作者： Khudhair, Kifah T. Najjar, Fallah H. Waheed, Safa Riyadh Al-Jawahry, Hassan M. Alwan, Haneen H. Al-Khaykan, Ameer Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Iraq Department of Computer System Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq Air Conditioning and Refrigeration Techniques Engineering Department Al-Mustaqbal University College Hillah Babylon51001 Iraq

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Acute lymphoblastic leukemia image segmentation based on modified HSV model

Acute lymphoblastic leukemia image segmentation based on mod...

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International Laser technology and Optics Symposium 2022, iLATOS 2022

作者： Najjar, Fallah H. Khudhair, Kifah T. Khudhair, Zaid Nidhal Alwan, Haneen H. Al-Khaykan, Ameer Department of Computer System Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Iraq Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Air Conditioning and Refrigeration Techniques Engineering Department Al- Mustaqbal University College Hillah Babylon51001 Iraq

Image segmentation is a critical step in computer-aided diagnosis that could speed up Leukemia detection. Leukemia is a cancer of the blood that has a reputation for being particularly lethal. Based on the immunohistochemical method, the leukocytes can be manually counted in a stained peripheral blood smear image to detect Acute Lymphoblastic Leukemia (ALL). Regrettably, the manual diagnosis process takes about 3 to 24 hours to complete, which is insufficient. This paper introduced a new and straightforward ALL image segmentation approach based on color image transformation. First, Leukemia, ALL-IDB1, ALL-IDB2, and ALL image datasets were used in this paper. The Leukemia dataset includes 208 ALL-IDB1 and ALL-IDB2 images, while The ALL dataset has 3256 images. Next, we use the HSV model to transform ALL images. In addition, we modified the HSV model by pre-processing the saturation channel for better results. Then, the pre-processed images were segmented based on a fixed threshold. After that, various metrics are utilized to measure the output of the proposed method. Finally, the proposed methodology is compared to currently used benchmarks. The proposed method outperforms previous approaches regarding accuracy, specificity, sensitivity, and time. In addition, results show that the proposed technique improves performance measures significantly. © Published under licence by IOP Publishing Ltd.

关键词： Image segmentation

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Realizing Global Citizen Solutions for Sustainable Development Goals: A Collaborative Engineering Course Between Thai and Japanese Students 23

Realizing Global Citizen Solutions for Sustainable Developme...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Sae-Jia, Thanachot Leelawat, Natt Tang, Jing Murakami, Rie Taoka, Yuki Ota, Eri Punyabukkana, Proadpran Chulalonkorn University Faculty of Engineering Department of Environmental Engineering Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan International School of Engineering Chulakongkorn University Faculty of Engineering Bangkok Thailand Global Education Section Center for Entrepreneurship Education Tokyo Institute of Technology Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350353952

One of the problems faced by engineering and science students is focusing solely on technical knowledge while needing more cultural context. In today's interconnected world, addressing global challenges necessitates collaborative efforts from individuals across nations. Accordingly, a joint course between Chulalongkorn University in Thailand and the Tokyo Institute of technology in Japan has been established. The recent theme of the course, 'Gamification towards Sustainable Development Goals (SDGs),' aimed to foster global citizenship and address SDGs through multicultural collaboration. This study examines the similarities and differences in problem identification between Thai and Japanese student groups participating in the course. Data was collected from discussions, presentations, and group assignments throughout the course. The findings indicate that while both groups share common concerns, such as waste management, language barriers, and transportation issues, their perspectives and proposed solutions are influenced by cultural characteristics. Thai students emphasized infrastructural inadequacies and local traditions, whereas Japanese students focused on efficiency and the impacts of tourism. The study highlights the importance of understanding cultural nuances in collaborative international projects to achieve effective and innovative solutions to global challenges. © 2024 IEEE.

关键词： Sustainable development goals

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A Novel Determination of Soluble NPK Nutrient Measurement system Using K-means and SOM Methods Compared with UV Visible Spectrophotometer

A Novel Determination of Soluble NPK Nutrient Measurement Sy...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat School of Engineering Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798350332360

Technologies are currently being used in smart farms to improve productivity. Agricultural innovation is also widespread. Therefore, the development of Internet of Things (IoTs) technologies and devices to collect data is important. Finding essential nutrients for growth such as Nitrogen (N), Phosphorus (P) and Potassium (K) are important factors. Competitiveness is the important key for growing vertically or indoors. However, soil testing can be expensive, complicated and time consuming. The development of devices that can effectively measure nutrient NPK levels could be used over time. This paper presents a suitable clustering using the K-means method and a forecasting method using the Self-Organizing Map (SOM) method to enhance the device's ability to analyze nutrients in real time. A total of 50,688 samples were tested using the K-Means clustering algorithm, and the sample data were also analyzed. A SOM was prepared to predict the accuracy of the analysis. A total of 11,486 samples were tested and analyzed, then the results of the soluble NPK nutrient monitoring system were taken. It has an efficiency of 84.15% compared to using a UV Visible spectrophotometer, allowing data to be predicted during nutrient solubility changes and to operate according to the proposed system. Therefore, the unsupervised machine learning will improve the measurement system of future cultivation systems. © 2023 IEEE.

关键词： Nutrients

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Handwritten Digit Classification based on Ensemble Majority Voting Classifier

Handwritten Digit Classification based on Ensemble Majority ...

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International Conference on systems and Control (ICSC)

作者： Mariam Alsharif Amina Abdo Kenz A Bozed Faculty of Information Technology University of Benghazi Benghazi Libya Departemnt of Artificial Intelligence Collage of Computer Technology-Benghazi Benghazi Libya Department of Computer System Design University of Benghazi Benghazi Libya

Handwritten digit recognition is a significant challenge in the field of machine learning, particularly for pattern recognition and computer vision applications. It has found applications in various areas, such as identifying digits on utility maps, postal mail zip codes, bank check amounts, and more. Offline handwritten digits possess distinct characteristics, including size, orientation, position, and thickness. Each person's handwriting is unique, making the classification process more difficult. Additionally, the high similarity between certain digits and the over fitting problem with high-dimensional data can increase computational time and cost. Consequently, numerous researchers have developed and implemented different machine learning algorithms to effectively address the issue of recognizing handwritten digits. This paper introduces a novel approach to improve the classification performance and accuracy of handwritten digit recognition. The proposed method utilizes an ensemble majority vote classifier, which combines three classifiers in each experiment by utilizing a range of different algorithms; Naïve Bayes (NB), Adaptive Boosting Algorithm (AdaBoost), K-Nearest Neighbors (K-NN), Multi-layer Perceptron Classifier (MLP), eXtreme Gradient Boosting algorithm (XGBoost), and Decision Tree Algorithm (DT) to form a single ensemble classifier. To validate the approach, we used the MINST free public handwritten dataset, to ascertain the classifier with the highest rate of accuracy in this study. The results obtained from the experiments demonstrated that the full performance of Ensemble model classifiers MLP, XGBOOST, and DT is superior to AdaBoost, K-NN, and NB. It achieved the highest accuracy level, reaching 98% in the experiment. The experimental results of this research showed that the recognition accuracy was improved compared to previous works.

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Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

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Microsystems & Nanoengineering 2022年第5期8卷 183-196页

作者： Nasim Bakhshaee Babaroud Merlin Palmar Andrada Iulia Velea Chiara Coletti Sebastian Weingärtner Frans Vos Wouter A.Serdijn Sten Vollebregt Vasiliki Giagka Department of Microelectronics Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyMekelweg 4Delft 2628 CDThe Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection TechnologiesFraunhofer Institute for Reliability and Micro-integration IZMGustav-Meyer-Allee 25Berlin 13355Germany Department of Imaging Physics Faculty of Applied ScienceDelft University of TechnologyLorentzweg 1Delft 2628 CJThe Netherlands Erasmus University Medical Center(Erasmus MC) dr.Molewaterplein 40Rotterdam 3015 GDThe Netherlands

Multimodal platforms combining electrical neural recording and stimulation,optogenetics,optical imaging,and magnetic resonance(MRI)imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific *** conductive,optically transparent,and MRI-compatible electrodes can optimally combine all *** as a suitable electrode candidate material can be grown via chemical vapor deposition(CVD)processes and sandwiched between transparent biocompatible ***,due to the high graphene growth temperature(≥900℃)and the presence of polymers,fabrication is commonly based on a manual transfer process of pre-grown graphene sheets,which causes reliability *** this paper,we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural *** fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and *** also exhibit the highest charge storage capacity(CSC)reported to date among all previously fabricated CVD graphene *** graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse ***,we show here,for the first time,that CVD graphene electrodes do not cause any image artifact in a 3T MRI *** results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal *** fabricated graphene electrodes enable multimodal neural recording,electrical and optogenetic stimulation,while allowing for optical imaging,as well as,artifact-free MRI studies.

关键词： transparent transfer process

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Deep Learning Image Transfer by Simulation 1

Deep Learning Image Transfer by Simulation

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2021

作者： Ammar, Abdussalam Masaud Youssef Ellafi, Amira Bozed, Kenz A. Zerek, Amer R. Faculty of Electronic Technology Tripoli Libya Benghazi University Faculty of Information Technology Computer System Design Dept. Benghazi Libya Zawia University Faculty of Engineering Ee Department Zawia Libya

ISBN: (纸本)9781665418560

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning with Neural style transfer that has been a high risk application for deep learning, make attention from and advertising the effectiveness to both the academic prisons and the general public. However, we have found by removal experiments that optimizing an image in the way neural style transfer does, we can even factor out the deepness (multiple layers of exchange linear and nonlinear transformations) all together and have neural style transfer working to a certain range. We present the VGG-19 Artistic Type Neural Algorithm, which can convert and recombine the image quality and style of natural images. This algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well known portrait. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks (CNN) and demonstrate their potential for high level image synthesis and manipulation. © 2021 IEEE.

关键词： Image classification

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An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

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computers, Materials & Continua 2021年第12期69卷 2967-2981页

作者： Arun Kumar Sivabalan Ambigapathy Mehedi Masud Emad Sami Jaha Sumit Chakravarty Kanchan Sengar Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Center for System Design Chennai Institute of TechnologyIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA

The article introduces Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC),known as hybrid waveform(NOMAFBMC),as two of the most deserving contenders for fifth-generation(5G)*** spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid *** compare the spectral efficiency of Orthogonal Frequency Division Multiplexing(OFDM),FBMC,NOMA,and *** is seen that the hybrid waveform outperforms the existing *** to Average Power Ratio(PAPR)is regarded as a significant issue in multicarrier *** combination of Selective Mapping-Partial Transmit Sequence(SLM-PTS)is an effective way to minimize large peak power *** SLM,PTS,and SLM-PTS procedures are applied to the NOMA-FBMC *** hybrid structure is applied to the existing ***,the correlated factors like Bit Error Rate(BER)and Computational Overhead(CO)are studied and computed for these *** outcome of the work reveals that the NOMA-FBMC waveform coupled with the SLM-PTS algorithm offers superior performance as compared to the prevailing systems.

关键词： 5G NOMA-FBMC SLM-PTS PAPR BER OFDM

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Enhanced Classification method for Large-Scale Medical Data

Enhanced Classification method for Large-Scale Medical Data

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Ahmed Hassan Khaled M Fouad Alaa Eldin Abdalla Yassin Somia Mohamed ElSewedy University of Technology Cairo Egypt Faculty of computer science and engineering New Mansoura University Mansoura Egypt Information System department Benha University Benha Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

The medical community has been concerned about how to increase the accuracy of different classification methods with large data that are being generated every day. The traditional KNN method has many limitations, such as dealing with large-scale data, handling uncertain data, and also determining the k parameter for KNN that gives the best result. In this research, three limitations are solved. By using optimized feature selection methods, optimal features are chosen from many irrelevant features. Then, uncertain data that has a conflict with the class label is handled. Finally, the optimal number of k in the KNN method that gives better accuracy is chosen using the ROC curve. The prime objective of this paper is to develop a hybrid optimal model for medical data classification that handles these challenges. The results are evaluated using the accuracy metric. Experimental results show that the enhanced KNN method outperforms the previously used KNN method in used medical datasets

关键词： Training Measurement Accuracy Computational modeling Data handling Nearest neighbor methods Ubiquitous computing Feature extraction Data models Testing

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