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Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms

Micromachines 2023年第9期14卷 1677-1677页

作者： Zameer, Aneela Naz, Sidra Raja, Muhammad Asif Zahoor Hafeez, Jehanzaib Ali, Nasir Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50–60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques. © 2023 by the authors.

关键词： Genetic algorithms

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Development of DWT–SVD based Digital Image Watermarking for Multi-level Decomposition 1

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2nd International Conference on Intelligent Computing, Information and Control Systems, ICICCS 2020

作者： Sana, Ehtesham Naaz, Sameena Ansari, Iffat Rehman Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi110062 India Electronics Engineering Section Faculty of Engineering and Technology University Women’s Polytechnic A.M.U. Aligarh UP202002 India

ISBN: (数字)9789811584435

ISBN: (纸本)9789811584428

In the age of digital data, the safety of digital content has gradually become paramount important. The demand for digital data in various forms such as text, images, video and audio has increased manifold. There are lots of vulnerabilities concerning digital data. It can easily be attacked, forged and manipulated to produce illegal copies. In the watermarking process, information, i.e., watermark image is inserted into a host data and extracted later in case of Copyright violation;owner of the content can demonstrate his ownership by regaining the embedded watermark. A watermark must be capable of being recovered even if a piece of content is modified or changed by several attacks. This paper uses frequency-domain technique, and the multi-level decomposition of the host image is done by using the combination of two-dimensional DWT (2D DWT) and SVD techniques. Here, the host/cover image is decomposed at six different levels separately, and the watermark image is inserted in the region of lowest frequency. After inserting a watermark, the host/cover image is reconstructed using an inverse DWT technique. By exposing the watermarked image to different image processing attacks such as pepper and salt, speckle and rotation, the robustness of various levels has been analyzed. The embedded watermark is extracted afterward and matched with the original one based on PSNR and MSE. The comparative experimental results show that the decomposition of the host image at the third level is found to be more suitable for watermarking among all other levels. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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An LMI adaptive-barrier function global sliding mode control of uncertain nonlinear systems with input saturation

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Ain Shams engineering Journal 2024年第2期15卷

作者： Rahimpour, Rahim Sepestanaki, Mohammadreza Askari Mobayen, Saleh Mokhtare, Zahra Fekih, Afef Assawinchaichote, Wudhichai Zhilenkov, Anton Electrical Engineering Department University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok10140 Thailand Department of Cyber-Physical Systems St. Petersburg State Marine Technical University Saint-Petersburg190121 Russia

This paper proposes a barrier function-based adaptive control strategy with a nonlinear sliding surface for a class of uncertain dynamic systems with input saturation. Its main objectives are to ensure robustness to parametric and non-parametric uncertainties whilst eliminating the reaching phase. A global nonlinear switching surface is defined and an adaptive barrier function sliding mode control approach is derived. The barrier strategy is adopted to ensure the convergence of the states to the desired values without overestimating the control gains. System stability is analyzed using the Lyapunov stability theory. The control parameters are obtained using the linear matrix inequality (LMI) approach. The control law is designed to remove the reaching phase. The effectiveness of the proposed approach was validated using a simulation study. The proposed control approach can easily be implemented to various nonlinear uncertain systems. © 2023 THE AUTHORS

关键词： Linear matrix inequalities

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Enhancing Quantum State Reconstruction with Structured Classical Shadows

arXiv

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arXiv 2025年

作者： Qin, Zhen Lukens, Joseph M. Kirby, Brian T. Zhu, Zhihui Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States DEVCOM Army Research Laboratory AdelphiMD20783 United States Tulane University New OrleansLA70118 United States

Quantum state tomography (QST) remains the prevailing method for benchmarking and verifying quantum devices;however, its application to large quantum systems is rendered impractical due to the exponential growth in both the required number of total state copies and classical computational resources. Recently, the classical shadow (CS) method has been introduced as a more computationally efficient alternative, capable of accurately predicting key quantum state properties. Despite its advantages, a critical question remains as to whether the CS method can be extended to perform QST with guaranteed performance. In this paper, we address this challenge by introducing a projected classical shadow (PCS) method with guaranteed performance for QST based on Haar-random projective measurements. PCS extends the standard CS method by incorporating a projection step onto the target subspace. For a general quantum state consisting of n qubits, our method requires a minimum of O(4n) total state copies to achieve a bounded recovery error in the Frobenius norm between the reconstructed and true density matrices, reducing to O(2nr) for states of rank r n—meeting information-theoretic optimal bounds in both cases. For matrix product operator states, we demonstrate that the PCS method can recover the ground-truth state with O(n2) total state copies, improving upon the previously established Haar-random bound of O(n3). Simulation results further validate the effectiveness of the proposed PCS method. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum optics

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric Pattern Recognition System Using Multi Resolution Approach

FPGA Implementation of Textural Feature Extractor and Distan...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： S. Hariprasath M. Santhi T.N. Prabakar V. Koushick Aravind. P T. Gunasekaran Department of ECE Saranathan College of Engineering Trichy Department of ECE Sri Sairam College of Engineering Anekal Bangalore Department of ECE Veltech Institute of Science and Technology Chennai Department of Electrical and computer Engineering Mattu University Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Sultanate of Oman

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation.

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Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon

arXiv

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arXiv 2025年

作者： Xu, Haodi Fan, Joshua Tao, Feng Jiang, Lifen You, Fengqi Houlton, Benjamin Sun, Ying Gomes, Carla Luo, Yiqi Soil and Crop Sciences Section School of Integrative Plant Science Cornell University IthacaNY United States Department of Computer Science Cornell University IthacaNY United States Department of Ecology and Evolutionary Biology Cornell University IthacaNY United States Department of Systems Engineering Cornell University IthacaNY United States

Big data and the rapid development of artificial intelligence (AI) provide unprecedented opportunities to enhance our understanding of the global carbon cycle and other biogeochemical processes. However, retrieving mechanistic knowledge from big data remains a challenge. Here, we develop a Biogeochemistry-Informed Neural Network (BINN) that seamlessly integrates a vectorized process-based soil carbon cycle model (i.e., Community Land Model version 5, CLM5) into a neural network (NN) structure to examine mechanisms governing soil organic carbon (SOC) storage from big data. BINN demonstrates high accuracy in retrieving biogeochemical parameter values from synthetic data in a parameter recovery experiment. We use BINN to predict six major processes regulating the soil carbon cycle (or components in process-based models) from 25,925 observed SOC profiles across the conterminous US and compared them with the same processes previously retrieved by a Bayesian inference-based PROcess-guided deep learning and DAta-driven modeling (PRODA) approach 1,2. The high agreement between the spatial patterns of the retrieved processes using the two approaches with an average correlation coefficient of 0.81 confirms BINN’s ability in retrieving mechanistic knowledge from big data. Additionally, the integration of neural networks and process-based models in BINN improves computational efficiency by more than 50 times over PRODA. We conclude that BINN is a transformative tool that harnesses the power of both AI and process-based modeling, facilitating new scientific discoveries while improving interpretability and accuracy of Earth system models. © 2025, CC BY.

关键词： Carbon capture and utilization

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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...

引用

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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Machine learning and data analysis for word segmentation of classical Chinese poems: illustrations with Tang and Song examples

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Digital Scholarship in the Humanities 2024年第1期39卷 228-241页

作者： Liu, Chao-Lin Chang, Wei-Ting Chu, Chang-Ting Zheng, Ti-Yong Department of Computer Science National Chengchi University 64 Chih-Nan Road Section 2 Wen-Shan Taipei 116011 Taiwan Department of Computer Science and Engineering University of California at Riverside 900 University Avenue Riverside 92521 CA United States Department of Electrical Engineering National Yang Ming Chiao Tung University 1001 University Road East Hsinchu 300093 Taiwan

Words are essential parts for understanding classical Chinese poems. We report a collection of 32,399 classical Chinese poems that were annotated with word boundaries. Statistics about the annotated poems support a few heuristic experiences, including the patterns of lines and a practice for the parallel structures (對仗), that researchers of Chinese literature discuss in the literature. The annotators were affiliated with two universities, so they could annotate the poems as independently as possible. Results of an inter-rater agreement study indicate that the annotators have consensus over the identified words 93 per cent of the time and have perfect consensus for the segmentation of a poem 42 per cent of the time. We applied unsupervised classification methods to annotate the poems in several different settings, and evaluated the results with human annotations. Under favorable conditions, the classifier identified about 88 per cent of the words, and segmented poems perfectly 22 per cent of the time. © 2024 Oxford University Press. All rights reserved.

关键词： Chinese literature classical Chinese poems digital humanities unsupervised learning word segmentation

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Photovoltaic induced self-powered gas sensor based on 2D MoS2 incorporated NbSe2 nanorods heterostructure for NH3 gas sensing at room temperature

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Chemical engineering Journal 2024年 491卷

作者： Saravanan, Adhimoorthy Huang, Bohr-Ran Hwang, Seung-Kyu Kathiravan, Deepa Wei-Wen Hsiao, Wesley Jayachitra, Ravichandran Abun, Abebaw Hong, Po-Da Mohammadi, Ali Vilian, A.T. Ezhil Han, Young-Kyu Suk Huh, Yun Graduate Institute of Electro-Optical Engineering and Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Department of Materials Science and Engineering National Taiwan University of Science and Technology 43 Keelung road Section 4 Taipei10607 Taiwan Department of Chemical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Biological Sciences and Bioengineering Nano Bio High-Tech Materials Research Center Inha University Incheon22212 Korea Republic of Department of Energy and Materials Engineering Dongguk University-Seoul Seoul100-715 Korea Republic of

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted significant attention for their optical and gas-sensing applications due to their exceptional sensitivity. Reliable selectivity and low power consumption are two major requirements for photodetector and gas sensor applications in next-generation electronic devices and the Internet of Things. Self-powered sensors (especially photovoltaic gas sensors) can solve these problems. In this study, for the first time, we report 2D TMDs (NbSe2-MoS2 hybrid) on a SiO2/Si substrate to fabricate photovoltaic self-powered gas sensors. The gas sensors are operated by the photovoltaic effect of the NbSe2-MoS2 nanostructure, which is prepared using the liquid phase exfoliation process. Initially, it was revealed that the present hybrid material exhibits photovoltaic properties under light illumination, with a circuit current of 0.25 µA and a circuit voltage of 34 mV. The NbSe2-MoS2 nanostructure characteristics were then used for NH3 gas sensing at different concentrations, and the gas sensing response was detected from low (8.8 % at 10 ppm) to high (28.8 % at 500 ppm) concentrations. The built-in electric field occurred between the NbSe2-MoS2 junction and eventually operated as a driving force for NbSe2-MoS2 gas sensing without an external bias voltage. The physisorption of gas molecules on their surface prompts a charge-transfer mechanism that improves the gas sensor response. The combined outcome of NbSe2-MoS2 heterostructures could pave way to next-generation gas sensing device fabrications. © 2024 Elsevier B.V.

关键词： Molybdenum compounds

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Comprehensive analysis on Ultralytics-supported YOLO models for detection and recognition of large office objects for indoor navigation

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Procedia computer science 2024年 246卷 3851-3858页

作者： Renann G. Baldovino Aron Jake P. Vidad Rudwin Paul B. Abastillas Nilo T. Bugtai Elmer P. Dadios Ryan Rhay P. Vicerra Argel A. Bandala Aaron Raymond See Nicanor R. Roxas Institute of Biomedical Engineering and Health Technologies (IBEHT) Department of Manufacturing Engineering and Management (DMEM) Department of Electronics and Computer Engineering (DECE) De La Salle University (DLSU) 2401 Taft Avenue 0922 Manila Philippines Department of Electrical Engineering Southern Taiwan University of Science and Technology (STUST) No. 1號 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

The state of object detection has quickly progressed due to the rapid development of hardware and deep learning models for the said task. The continuous development of the latter allowed its application in real-time. That is, from two-stage to one-stage detectors, which greatly reduced the processing time; and from anchor-based to anchor-free detectors, which allowed a more efficient and flexible model implementation. The purpose of this study is to explore the various YOLO versions available in Ultralytics to detect large office objects for obstacle avoidance. More specifically, this study observed how the versions, 3u, 5mu, 8 and 6m, from which the first three are anchor-free, perform over the dataset. Based from the results, it was found that there were no significant differences between the detectors, except for the anchor-based v6m, which has the worst scores. The four models, v8n, v8s, v8m, and v5mu, were, however, chosen as the candidate models due to their processing speed, ranging from 66 to 75 FPS on the NVIDIA GeForce RTX 3060 GPU. The said models were also subjected to an optimization process in terms of the confidence and IoU threshold values to find the pair that could provide the best metric scores. Results have provided candidate models for the study’s detection task for obstacle avoidance in real-time.

关键词： deep learning indoor navigation object recognition YOLO

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