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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Pratiwi, Heni Ispur Kartowisastro, Iman Herwidiana Soewito, Benfano Budiharto, Widodo Doctorate Program School of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University Binus Graduate Program Doctorate Program of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta Indonesia Bina Nusantara University Computer Science Binus Graduate Program Computer Science Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350399073

The voice patterns of gender and age have been used as speaker's identity and implied into sectors such as smart cards, healthcare, banking, and other security access controls. However, age, illness, and ambient noise factors exist challenging issues of voice changes in speech recognition systems to be resolved. This paper presents experiment results of observing those issues, and its goal was obtaining the elements of voice features to secure the speaker's identity. Mel-frequency Cepstral Coefficient (MFCC) was used to conduct observations on spectral centroid and bandwidth, it has effectively performed capturing features of individual speech signal. The experiment was conducted with data split into three groups: under 13, range 13 to 19, and above 19 years old for both male and female. The results showed that both gender under 13 have centroid voices reached to 98%, age of 13 to 19 remain with centroid reached 98% of bandwidth for male and passing over 100% for female, then above 19 for both genders speak lower with centroid 50% of bandwidth, and male above age of 19 remains to keep centroid about 50% and female changes to 95%. MFCC fits to expose the speech spectrum for the purpose of an analysis frame on specific local spectral properties of voices, and the finding is to form a security line or an individual profiling adoption. © 2022 IEEE.

关键词： Bandwidth

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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Design, Mathematical Modeling, and Control of an Underactuated 3-DOF Experimental Helicopter

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IEEE Access 2024年 12卷 55568-55586页

作者： Makki, Osamah Talal Moosapour, Seyyed Sajjad Mobayen, Saleh Nobari, Jafar Heyrani Shahid Chamran University of Ahvaz Faculty of Engineering Ahvaz*** Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu Yunlin640301 Taiwan K. N. Toosi University of Technology Department of Electrical and Computer Engineering Tehran19697 Iran

This article covers the design, implementation, mathematical modelling, and control of a multivariable, underactuated, low-cost, three-degrees-of-freedom experimental helicopter system (namely a 3-DOF helicopter). The system is considered an essential benchmark for the development and evaluation of control algorithms and identification methods as it mimics real helicopters and UAVs. The system is multivariable and difficult to control due to its underactuation characteristics. In addition, it presents further challenges in modelling and control analysis in terms of high nonlinearity, coupling, uncertainty, and unmodeled dynamics. In this scene, the system is designed, developed, and operated virtually in SolidWorks using the necessary hardware and software. A new accurate multivariable coupled nonlinear mathematical model is derived and proposed. The model takes into account the complex dynamics and interactions between the system components, providing a more comprehensive representation of the real system compared to previous models. System parameters are then identified using the Sum of the Square Error (SSE) algorithm, and the mathematical model is compared to the practical results in many different scenarios via Simulink. Experimental tests confirmed the mathematical model and robust performance of the implemented experimental 3-DOF helicopter system, as well as the ability of the proposed controller to stabilize the helicopter and track different trajectories in the presence of uncertainties and cross-coupling effects. © 2013 IEEE.

关键词： Iron

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Movable Superdirective Pairs: A Phase Shifter-Free Approach to mmWave Communications

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IEEE Wireless Communications Letters 2025年第6期14卷 1806-1810页

作者： Han, Liangcheng Yin, Haifan Gao, Mengying Zhang, Rui Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance millimeter-wave (mmWave) communications. By controlling the rotation angles and positions of superdirective antenna pairs via low-power actuators on the user side, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without the need for phase shifters, thereby reducing hardware costs and insertion losses. An efficient algorithm based on alternating optimization and gradient projection is developed to solve the non-convex rotation–position design problem. Moreover, MSP is shown to be robust in both line-of-sight (LOS) and non-line-ofsight (NLOS) channels. Simulation results demonstrate that MSP achieves significant performance gains over fixed-position arrays, while maintaining low system complexity. © 2012 IEEE.

关键词： Microwave antennas

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Repurposing transfer learning strategy of computer vision for owl sound classification 7

Repurposing transfer learning strategy of computer vision fo...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Gunawan, Kevin William Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Accurately predicting an owl species based on its sound can be helpful for owl conservation. To build an accurate model for owl sound classification, deep learning is currently the most preferred algorithm, due to its excellent performance for modeling audio data. However, deep learning is generally underperformed for a small dataset, which is the case for recognizing scops owl sound. To overcome the issue, we proposed a transfer learning strategy, which is common for computer vision tasks, that can alleviate overfitting in a deep learning model for the owl sound classification. In our approach, we propose a neural network architecture consisting of the backbone of a EfficientNet model pre-Trained on the massive ImageNet database. The model takes the sound input that has been converted as two image representations: Spectrogram and Mel Frequency Cepstral Coefficients. Our strategy enables the use of a relatively small size of pre-Trained image classification model, which is widely available, for transfer learning in owl sound classification. Deploying the lightweight model in an automatic sound classifier provides a fast and accurate tool for various owl conservation purposes. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Universally Instance-Optimal Mechanisms for Private Statistical Estimation 37

Universally Instance-Optimal Mechanisms for Private Statisti...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Asi, Hilal Duchi, John C. Haque, Saminul Li, Zewei Ruan, Feng Apple United States Departments of Statistics and Electrical Engineering Stanford University United States Department of Computer Science Stanford University United States Department of Statistics and Data Science Northwestern University United States

We consider the problem of instance-optimal statistical estimation under the constraint of differential privacy where mechanisms must adapt to the difficulty of the input dataset. We prove a new instance specific lower bound using a new divergence and show it characterizes the local minimax optimal rates for private statistical estimation. We propose two new mechanisms that are universally instance-optimal for general estimation problems up to logarithmic factors. Our first mechanism, the total variation mechanism, builds on the exponential mechanism with stable approximations of the total variation distance, and is universally instance-optimal in the high privacy regime Ε ≤ 1/√n. Our second mechanism, the T-mechanism, is based on the T-estimator framework (Birgé, 2006) using the clipped log likelihood ratio as a stable test: it attains instance-optimal rates for any Ε ≤ 1 up to logarithmic factors. Finally, we study the implications of our results to robust statistical estimation, and show that our algorithms are universally optimal for this problem, characterizing the optimal minimax rates for robust statistical estimation. © 2024 H. Asi, J.C. Duchi, S. Haque, Z. Li & F. Ruan.

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Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions 2

Enhancing UAV Path Planning Efficiency through Adam-Optimize...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Akshya, J. Neelamegam, G. Sureshkumar, C. Nithya, V. Kadry, Seifedine SRM Institute of Science and Technology Department of Computational Intelligence Chennai603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore641032 India Noroff University College Department of Applied Data Science Kristiansand4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Trajectories

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Wheat Disease Recognition Based on a Fusion Model with Improved Spatial and Channel Reconstruction Convolution Combined with Contextual Transformer 5

Wheat Disease Recognition Based on a Fusion Model with Impro...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Yang, Zicong Jin, Aitong Wang, Junxi Xie, Haifan Zhan, Xin Li, Qiaolin Zhuhai College of Science and Technology Department of Computer Zhuhai China Zhuhai College of Science and Technology Department of Statistics and Data Science Zhuhai China Zhuhai College of Science and Technology Department of Mechanical Engineering Zhuhai China Fujian University of Traditional Chinese Medicine School of Traditional Chinese Medicine Fuzhou China

ISBN: (纸本)9798350373820

Crop diseases cause loss of agricultural products and have a critical impact on society, the economy, and the environment, so it is crucial for early defending the wheat disease. However, seeking an expert diagnosis can contribute to delays and higher costs. Therefore, this paper proposes a deep learning neural network with improved spatial and channel reconstruction convolution combined with CoT Attention module, named SCCoT-WheatNet. This innovated neural network can recognize wheat disease. The experimental dataset was obtained from the Internet and some publicly available datasets using web crawler was technology. This modification is based on the VGG13 model. In the final two sections of the VGG13 model, feature extraction replaces conventional convolutional layers with Contextual Transformer module and spatial and channel reconstruction convolution block. The experiment adopts a cross-fold approach. In the first fold test, the results show that the average recognition accuracy of the SCCoT-WheatNet network is 9 6. 9 4%, which is higher than the classical VGG network and the Vision Transformer models of the last two years. © 2024 IEEE.

关键词： Convolution

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Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

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Soft Computing 2024年第19期28卷 11633-11646页

作者： Amiya, Gautam Murugan, Pallikonda Rajasekaran Ramaraj, Kottaimalai Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fracture

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Monolithic Convex Limiting for Legendre-Gauss-Lobatto Discontinuous Galerkin Spectral-Element Methods

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Communications on Applied Mathematics and Computation 2024年第3期6卷 1860-1898页

作者： Andrés M.Rueda-Ramírez Benjamin Bolm Dmitri Kuzmin Gregor J.Gassner Department of Mathematics and Computer Science University of CologneWeyertal 86-90Cologne 50931Germany Institute of Applied Mathematics(LS Ⅲ) TU Dortmund UniversityVogelpothsweg 87Dortmund 44227Germany Center for Data and Simulation Science University of CologneWeyertal 86-90Cologne 50931Germany

We extend the monolithic convex limiting(MCL)methodology to nodal discontinuous Galerkin spectral-element methods(DGSEMS).The use of Legendre-Gauss-Lobatto(LGL)quadrature endows collocated DGSEM space discretizations of nonlinear hyperbolic problems with properties that greatly simplify the design of invariant domain-preserving high-resolution *** to many other continuous and discontinuous Galerkin method variants,a particular advantage of the LGL spectral operator is the availability of a natural decomposition into a compatible subcellﬂux *** a highorder spatial semi-discretization in terms of intermediate states,we performﬂux limiting in a manner that keeps these states and the results of Runge-Kutta stages in convex invariant *** addition,local bounds may be imposed on scalar quantities of *** contrast to limiting approaches based on predictor-corrector algorithms,our MCL procedure for LGL-DGSEM yields nonlinearﬂux approximations that are independent of the time-step size and can be further modiﬁed to enforce entropy *** demonstrate the robustness of MCL/DGSEM schemes for the compressible Euler equations,we run simulations for challenging setups featuring strong shocks,steep density gradients,and vortex dominatedﬂows.

关键词： Structure-preserving schemes Subcellﬂux limiting Monolithic convex limiting(MCL) Discontinuous Galerkin spectral-element methods(DGSEMS) Legendre-Gauss-Lobatto(LGL)nodes

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