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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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Delay Analysis of Routing Protocols for WSN

Delay Analysis of Routing Protocols for WSN

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Rahil Bensaid Adel Ben Mnaouer Hatem Boujemaa ENIG National Engineering School of Gabes Tunisia Faculty of Engineering Applied Science and Technology Canadian University Dubai UAE College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia UCAR COSIM Lab. Higher School of Communications of Tunis Tunisia

In this paper, we explore the benefits of cooperative diversity for Wireless Sensor Network (WSN). Relays are uniformily distributed in $L$ hops and $N$ branches between the sensor nodes SN and the base station BS. Three cooperative routing protocols are considered, namely: one hop, suboptimal and optimal routing protocols. One hop routing chooses the best relay in each hop. Optimal routing selects the best path between SN and BS. Suboptimal routing decomposes the network into $K$ subnetworks and selects successively the best path in each one. We derive the outage probability, the Packet Error Probability (PEP) and the queening delays spent in the system for each protocol.

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The Role of Heterogeneity in Autonomous Perimeter Defense Problems

arXiv

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arXiv 2022年

作者： Adler, Aviv Mickelin, Oscar Ramachandran, K. Sukhatme, Gaurav S. Karaman, Sertac Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Program in Applied and Computational Mathematics Princeton University Princeton NJ08544 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States

When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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A Multi-Dimensional Mathematical Model for Surface Exerted Point Forces in Elastic Media

arXiv

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arXiv 2024年

作者： Asghar, Sabia Peng, Qiyao Vermolen, Fred J. Department of Mathematics and Statistics Computational Mathematics Group University of Hasselt Diepenbeek Belgium University of Hasselt Diepenbeek Belgium Mathematical Institute Leiden University Leiden Netherlands Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Mathematics and Applied Mathematics University of Johannesburg Johannesburg South Africa Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

Some phenotypes of biological cells exert mechanical forces on their direct environment during their development and progression. In this paper the impact of cellular forces on the surrounding tissue is considered. Assuming the size of the cell to be much smaller than that of the computational domain, and assuming small displacements, linear elasticity (Hooke’s Law) with point forces described by Dirac delta distributions is used in momentum balance equation. Due to the singular nature of the Dirac delta distribution, the solution does not lie in the classical H1 finite element space for multi-dimensional domains. We analyze the L2-convergence of forces in a superposition of line segments across the cell boundary to an integral representation of the forces on the cell boundary. It is proved that the L2-convergence of the displacement field away from the cell boundary matches the quadratic order of convergence of the midpoint rule on the forces that are exerted on the curve or surface that describes the cell boundary. © 2024, CC BY.

关键词： Cytology

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A New Prediction Model for Lung Cancer Based on ANN-SVM 5

A New Prediction Model for Lung Cancer Based on ANN-SVM

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5th International Conference on computer Information science and Application technology, CISAT 2022

作者： Li, Hua Lu, Yongchen Wang, Junyi Zhang, Yue Faculty of Chemical Engineering Beijing University of Chemical technology 15 Beisanhuan Dong Lu Chaoyang District Beijing100029 China School of Information Studies Syracuse University 900 South Crouse Ave SyracuseNY13244 United States Faculty of Computer Science and Technology Harbin Institute of Technology 2 Wenhua Xi Lu Huancui District Weihai264200 China Faculty of applied physics Jinan University 601 Huangpu Dadao Xi Tianhe District Guangzhou510632 China

ISBN: (纸本)9781510660076

Lung cancer has become a major threat to human life and health due to its characteristics of difficulty in early diagnosis and high mortality. Therefore, to develop a reliable method to predict lung cancer is very important. This study provides a new prediction method based on artificial intelligence technology. Firstly, a data set of patients' physical health status was collected, and the correlation between various variables and lung cancer was analyzed using random forest model. Then, the data sets were brought into some common data sets for training and the accuracy of these models was obtained. Then, the neural network technology is used to preliminarily process the data into feature vectors, and then the feature vectors are input into the support vector machine model, and the ANN-SVM hybrid model is established and trained. The results of this model are optimized by adjusting various model parameters and compared with the results of other common prediction models. Finally, a lung cancer prediction APP is made by using this model. Random forest results show that gender, alcohol consuming, and peer pressure are the main factors leading to lung cancer. The prediction accuracy of Ann-SVM model is up to 95.1613% shown by the cross-validation, which is higher than 2.8780%-8.5446% compared with other common prediction models, indicating that the model and prediction APP have practical value. © 2022 SPIE.

关键词： Decision trees

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A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methods

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GAMM Mitteilungen 2024年第1期47卷 e202370002-e202370002页

作者： Balzani, Daniel Heinlein, Alexander Klawonn, Axel Knepper, Jascha Nurani Ramesh, Sharan Rheinbach, Oliver Saßmannshausen, Lea Uhlmann, Klemens Civil and Environmental Engineering Ruhr University Bochum Bochum Germany Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Department of Mathematics and Computer Science University of Cologne Cologne Germany Center for Data and Simulation Science University of Cologne Cologne Germany Fakultät für Mathematik und Informatik Technische Universität Bergakademie Freiberg Freiberg Germany Technische Universität Bergakademie Freiberg Freiberg Germany

A computational framework is presented to numerically simulate the effects of antihypertensive drugs, in particular calcium channel blockers, on the mechanical response of arterial walls. A stretch-dependent smooth muscle model by Uhlmann and Balzani is modified to describe the interaction of pharmacological drugs and the inhibition of smooth muscle activation. The coupled deformation-diffusion problem is then solved using the finite element software FEDDLib and overlapping Schwarz preconditioners from the Trilinos package FROSch. These preconditioners include highly scalable parallel GDSW (generalized Dryja–Smith–Widlund) and RGDSW (reduced GDSW) preconditioners. Simulation results show the expected increase in the lumen diameter of an idealized artery due to the drug-induced reduction of smooth muscle contraction, as well as a decrease in the rate of arterial contraction in the presence of calcium channel blockers. Strong and weak parallel scalability of the resulting computational implementation are also analyzed. © 2024 The Authors. GAMM - Mitteilungen published by Wiley-VCH GmbH.

关键词： Finite element method

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Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon's Adamaoua region

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Intelligence-Based Medicine 2025年 11卷 100220-100220页

作者： Naroum, Esaie Maka, Ebenezer Maka Abboubakar, Hamadjam Dayang, Paul Bamana, Appolinaire Batoure Garga, Benjamin Daouda, Hassana Daouda Bakouri, Mohsen Khan, Ilyas University of Ngaoundéré Department of Mathematics and computer Science Faculty of Science P.O. Box 455 Ngaoundéré Cameroon University of Douala National Higher Polytechnic School Department of Computer Engineering and Telecommunications P.O. Box 2701 Douala Cameroon University of Ngaoundéré University Institute of Technology Department of Computer Engineering P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré Laboratory of Analysis Simulation and Test (LASE) P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré School of Geology and Mining Engineering Department Applied Mathematics and Computer Science P.O. Box 115 Meiganga Cameroon University of Ngaoundéré ENSAI Department of Electrical Electronic and Automatic Engineering P.O. Box 455 Ngaoundéré Cameroon Majmaah University College of Applied Medical Science Department of Medical Equipment Technology Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia

The Plasmodium parasite, which causes malaria is transmitted by Anopheles mosquitoes, and remains a major development barrier in Africa. This is particularly true considering the conducive environment that promotes the spread of malaria. This study examines several machine learning approaches, such as long short term memory (LSTM), random forests (RF), support vector machines (SVM), and data regularization models including Ridge, Lasso, and ElasticNet, in order to forecast the occurrence of malaria in the Adamaoua region of Cameroon. The LSTM, a recurrent neural network variant, performed the best with 76% accuracy and a low error rate (RMSE = 0.08). Statistical evidence indicates that temperatures exceeding 34 degrees halt mosquito vector reproduction, thereby slowing the spread of malaria. However, humidity increases the morbidity of the condition. The survey also identified high-risk areas in Ngaoundéré Rural and Urban and Meiganga. Between 2018 and 2022, the Adamaoua region had 20.1%, 12.3%, and 10.0% of malaria cases, respectively, in these locations. According to the estimate, the number of malaria cases in the Adamaoua region will rise gradually between 2023 and 2026, peaking in 2029 before declining in 2031. © 2025 The Authors

关键词： Adamaoua-Cameroon Deep learning Machine learning Malaria Metric evaluation Time-series forecasting

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Dopant ions size impact on structural properties of ordered poly(3,4-ethylenedioxythiophene) systems

Dopant ions size impact on structural properties of ordered ...

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作者： Şniechowski, Maciej Kozik, Tomasz A. Luzny, Wojciech Faculty of Physics and Applied Computer Science AGH University of Science and Technology Al. Mickiewicza 30 Cracow30-059 Poland

The purpose of this work was to determine whether the structures of ordered part of novel poly(3,4-ethylenedioxythiophene) PEDOT : OTf (doped with trifluoromethanesulfonate anions) and PEDOT : HSulf (doped with hydrogensulfate anions) systems could really be simply the layered structure of PEDOT : Tos (doped with tosylate anions) with the counter ion changed to the appropriate one. Insight into this problem could be provided by molecular dynamics simulations but also by swarm intelligence optimization, developed earlier for the purpose of investigating the other structures. © 2020 Industrial Chemistry Research Institute. All rights reserved.

关键词： Sulfur compounds

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Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA

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Health Data science 2021年第1期2021卷 78-87页

作者： Raj Dandekar Emma Wang George Barbastathis Chris Rackauckas Department of Computational Science and Engineering Massachusetts Institute of TechnologyCambridgeUSA MA 02139 Department of Electrical Engineering and Computer Sciences Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 138602 Department of Applied Mathematics Massachusetts Institute of TechnologyCambridgeMA 02139USA

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020,there is an urgent need to develop robust,data-driven models to quantify the effect which early reopening had on the infected case count *** particular,it is imperative to address the question:How many infected cases could have been prevented,had the worst affected states not reopened early?To address this question,we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network *** model decomposes the contribution of quarantine strength to the infection time series,allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in *** show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our ***,our results demonstrate that in the event of a stricter lockdown without early reopening,the number of active infected cases recorded on 14 July could have been reduced by more than 40%in all states considered,with the actual number of infections reduced being more than 100,000 for the states of Florida and *** we continue our fight against COVID-19,our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution,for any region under consideration.

关键词： COV neural Central

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Measuring the Enablers of University-based Techno-Entrepreneurship Education to Achieve Sustainable Business: A New-Age Analytical Approach

Measuring the Enablers of University-based Techno-Entreprene...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Heena Siroya Manish Dadhich Disha Mathur Mamta Jain Arvind Sharma Kamal Kant Hiran School of Management Sir Padampat Singhania University Udaipur India Department of EAFM Commerce University of Rajasthan Jaipur India School of Engineering Sir Padampat Singhania University Udaipur India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Indore India

Both academics and professionals are becoming increasingly curious about how to measure the impact of Techno-entrepreneurship programs in higher education. This research aimed to provide light on how collegiate Techno-entrepreneurship programs affect students' propensity to go into business by integrating passion and competency-based approaches. A structured questionnaire using a Likert scale and a purposive sample technique was used to gather and analyze the data from 140 technical and non-technical students. The study employed Smart-PLS to establish a model using entrepreneurial intentions, entrepreneurial attributes, entrepreneurial passion, and Techno-entrepreneurship education, which leads to sustainable business. The study's results show that Techno-entrepreneurship education mediates in achieving sustainable business. The originality of this study resides in its investigation of the model that explains the nexus of chosen variables based on previous studies and expanded areas to achieve sustainable business. The research findings are expected to influence how Techno-entrepreneurship is taught by nurturing sustainable development.

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