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12th IEEE International Conference on information and Communication Networks, ICICN 2024

作者： He, Quanlin Li, Qiming Yang, Hongyan Yang, Yuhang Sun, Hongrui Li, Jianqing Chen, Yunyi School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9798350355802

Metasurface is a kind of artificial layered material whose thickness is less than the wavelength, which can flexibly and effectively control the polarization, amplitude, phase, polarization mode and propagation mode of electromagnetic waves. In this paper, a split ring metasurface structure with two openings is designed to excite Fano resonance by breaking the symmetry of the structure. Based on the finite element method (FEM), the asymmetric splitting ring of Thz metsurface is studied. It is shown that the peak value of Fano resonance and the quality factor (Q) can be modulated by changing the inner and outer diameters of the splitting ring. The Fano resonance is very sensitive to the asymmetry of the structure. A small asymmetry will excite the Fano resonance with high intensity, high quality factor and very narrow resonance linewidth. The refractive index sensitivity of the designed sensor reaches 7.23THz/RIU, and the Q value is up to 93.33. It can be used as a sensor to monitor the ambient refractive index, ambient temperature and liquid concentration. © 2024 IEEE.

关键词： Terahertz waves

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model 5

Diabetic Foot Ulcer Detection and Classification using ConvM...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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Transformer Based Advanced Multi-Target Tracking with Guiding Mechanism 13

Transformer Based Advanced Multi-Target Tracking with Guidin...

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13th International Conference on Control, Automation and information sciences, ICCAIS 2024

作者： Zhang, Linao Xiong, Yuhuan Wei, Xinwei Wan, Xiaolong Zheng, Sinuo Yi, Wei School of Information and Communication Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798331542047

In practice, the nonlinearity of the observation model increases the complexity of the Multi-target Tracking (MTT) problem. Compared to model-based methods, deep learning (DL) is able to learn complex relationships in the data through training, and thus is gradually gaining attention in the field of MTT. Different training data can have a significant impact on network models, but there are limited studies that consider the impact of feature differences in nonlinear measurements on network models. To realize an advanced DL-based MTT method for nonlinear systems, we propose a range guiding mechanism that models discrepancies in nonlinear measurement data using target range information, thereby enhancing the robustness and consistency of the features. Our innovative Range Guiding Tracking with Transformer (RGTT) method introduces a range guiding module between the Transformer encoder and decoder to enhance features with range information while regressing the predicted state of the current frame. By continuously analyzing the nonlinear measurements fed into the RGTT, the continuous predicted state of targets in the Cartesian coordinate system can be obtained. Experimental results show that the guiding mechanism enables RGTT to deeply grasp measurement features, resulting in excellent tracking performance and generalization ability. © 2024 IEEE.

关键词： Nonlinear simulations

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Improvisation in Water Quality Prediction using H20 ViML: A comparative study

Improvisation in Water Quality Prediction using H20 ViML: A ...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mallika Singh, Nanhay Lathar, Pankaj Netaji Subhas University of Technology Department of Computer Science & Engineering East Campus Delhi India Delhi Skill & Entrepreneurship University School of Information Technology & Information Technology Enabled Services Delhi India

ISBN: (纸本)9798350373783

Water is the source of life. The concern however rounds about to availability of safe and healthy drinking water to beings. With increasing industrialization and urbanization pollution levels are increasing. Relying on age old long calculative methods for water quality prediction is simply wastage of our resources. Nowadays collected data is also a resource. If we use Machine Learning models, dependency on manual calculation and identification of degraded parameter can pave way to enhancement. This can be a breakthrough in global concern of water management. Currently Water Quality Indexing is used which is a lengthy, data dependent and trouble some process. Machine Learning models for regression to increase accuracy can be used. Comparison can be drawn between Random forest, XG Boost, Decision tree and other regression focused algorithm, Further using ensemble models using H2O ML stacked ensemble automation accuracy can be increased. Further proper identification of parameters can be achieved by using genetic algorithms such as PSO, sparrow searching. The main objective is to improve accuracy and broaden the spectrum of parameter consideration using Machine Learning algorithms. © 2024 IEEE.

关键词： Genetic algorithms

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Memristive oscillator to memristive map,energy characteristic

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science China(Technological sciences) 2024年第5期67卷 1567-1578页

作者： GUO YiTong MA Jun ZHANG XiaoFeng HU XiKui College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou730050China Department of Physics Lanzhou University of TechnologyLanzhou730050China School of Science Chongqing University of Posts and TelecommunicationsChongqing400065China

Most of nonlinear oscillators composed of capacitive and inductive variables can obtain the Hamilton energy by using the Helmholtz theorem when the models are rewritten in equivalent vector *** energy functions for biophysical neurons can be obtained by applying scale transformation on the physical field energy in their equivalent neural *** dynamical systems often have exact energy functions,while some mathematical models just suggest generic Lyapunov functions,and the energy function is effective to predict mode *** this paper,a memristive oscillator is approached by two kinds of memristor-based nonlinear circuits,and the energy functions are defined to predict the dependence of oscillatory modes on energy *** absence of capacitive variable for capacitor,the physical time t and charge q are converted into dimensionless variables by using combination of resistance and inductance(L,R),e.g.,τ=t×R/*** energy function for each memristive map is proposed by applying the similar weights as energy function for the memristive *** example,energy function for the map is obtained by replacing the variables and parameters of the memristive oscillator with corresponding variables and parameters for the memristive *** memristive map prefers to keep lower average energy than the memristive oscillator,and chaos is generated in a discrete system with two *** scheme is helpful for energy definition in maps,and it provides possible guidance for verifying the reliability of maps by considering the energy characteristic.

关键词： Hamilton energy memristor Helmholtz theorem scale transformation memristive circuit

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Analyzing Real-Time Applied Applications in Silicon on Chip

Analyzing Real-Time Applied Applications in Silicon on Chip

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Govind Shay Harron, Syed Singh, Akhilendra Pratap Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

this paper gives a singular method for analyzing actual time-implemented applications in silicon-on-chip (SOC) architectures. The proposed technique uses a mixture of analytical answers and hardware degree simulations to benefit insights on system-level performance. An in-depth dialogue of the implementation and testing of the proposed approach is furnished, along with a detailed description of the simulation outcomes. The paper highlights the ability of a combination of analytical and hardware simulators to understand the machine's behavior better and attain a more accurate estimation of machine performance. Numerous examples reveal the effectiveness of the proposed technique and the insights obtained from the simulation consequences. In the end, the paper identifies several capability areas for destiny research. © 2024 IEEE.

关键词： Description Estimation Identifies Implemented Numerous Simulation

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Multivariate Statistical Analysis on Hyper Spectral Satellite Images for Land Cover Mapping

Multivariate Statistical Analysis on Hyper Spectral Satellit...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Dheer, Meenakshi Adlin Jebakumari, S. Singh, Shweta Vivekananda Global University Department of Agriculture Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper analyzes hyperspectral satellite tv for pc snap shots for land cover mapping with multivariate statistical analysis (MSA). It describes the method of mapping land cowl and how the components containing the extraordinary land cowl lessons are recognized via MSA. The analysis of the facts considers the visible, near-infrared, and shortwave infrared spectra of the Landsat image facts. The diverse MSA techniques which might be used for identifying land cover kinds, such as significant thing evaluation, unbiased aspect analysis, linear discriminant analysis, multi-dimensional scaling, cluster evaluation, and correlation analysis, are explained in detail. The advantages of using MSA over conventional techniques also are mentioned. Eventually, the results are compared with the overall performance of MSA on particular land cowl sorts. It's miles concluded that MSA is a dependable technique to land cover mapping with hyperspectral satellite tv for pc pics. Multivariate statistical evaluation on hyperspectral satellite pictures offers an expansion of possibilities to categorize land cowl and resources in mapping numerous capabilities on the Earth. Such techniques consist of linear discriminant evaluation, fundamental aspect evaluation, independent component evaluation, Multivariate selection timber, Kernel Discriminant evaluation, and extra. Those fashions extract extensive statistical features from the pics, permitting more accuracy in detecting functions or classes of land cowl. Many of these techniques can also be integrated with different techniques and tree-primarily based classifiers to refine the land cover type further. Furthermore, these methods may be used along with remotely sensed data, including topographic maps, to provide extra insight into land cover's spatial and temporal characteristics. In precis, hyperspectral satellite tv for pc imagery offers a powerful device for knowledge of the Earth's surface, and multivariate statistical methods substanti

关键词： capabilities hyperspectral mapping recognized significant

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Automated Static Analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks

Automated Static Analysis with Bayesian Inference for Interf...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shankar, Shiv Shankar Mehta, Deepak Singh, Vaishali Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper researches modern automated Static evaluation software with Bayesian Inference for interference mitigation in 5G cloud networks. The automatic static analysis approach affords a novel technique to discover the interference between the community nodes. The proposed studies a new modern Bayesian Inference to confirm the interference degree between nodes after the automated static evaluation method is widely recognized. The Bayesian Inference method is particularly beneficial, as it may provide sturdy statistical records approximately the level of contemporary interference among nodes. The proposed research evaluated the use of a couple of experiments, demonstrating the state-of-the-art method's accuracy and effectiveness. The outcome of today's research affords an efficient approach for interference mitigation in 5G cloud networks. It presents an automatic Static analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks, primarily based on system-gaining knowledge state modern and superior evaluation of modern 5G networks. The principle objective present day this technique is to reduce the signal-to-interference ratio (SIR) so that you can improve the overall performance of ultra-modern 5G cloud networks. Especially the proposed method of brand new Bayesian Inference to successfully examine the signal propagation sample state-of-the-art 5G network. Then, the learned values from the Bayesian Inference are used to optimize the inter-cell interference cancellation (IICC), inter-cell interference coordination (ICIC), and interference mitigation algorithms. ultimately, experimental effects reveal the effectiveness of today's the proposed method in terms of modern-day advanced community performance and decreased SIR. The outcomes recommend that the proposed automated static analysis with Bayesian Inference for Interference Mitigation in 5G Cloud Networks can offer an efficient and dependable answer for optimizing overall performance in 5G

关键词： automatic Bayesian contemporary evaluation mitigation

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USCNet: Seismic P-wave arrival time pickup based on deep learning 11

USCNet: Seismic P-wave arrival time pickup based on deep lea...

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11th International Conference on Environmental and engineering Geophysics, ICEEG 2024

作者： Xu, Lei Shen, Tong Jiang, Xuan Country School of Information and Engineering Southwest University of Science and Technology Mianyang China

The accurate pickup of seismic P-waves has important application value for real-time seismic data processing such as earthquake activity monitoring and earthquake disaster warning. However, existing low signal-to-noise ratio seismic data pickup algorithms often face problems of low accuracy and poor robustness. To address these issues, we designed a seismic P-wave picking network called USCNet. This network combines the advantages of U-Net and SegNet networks. The U-Net network performs well in picking accuracy, but requires a large number of learning parameters;SegNet networks are trained with fewer learning parameters, but have lower picking accuracy. The USCNet network integrates the CBAM attention mechanism, which can deeply extract key information from seismic data, thereby significantly improving the picking accuracy. We use the Stanford Earthquake Dataset (STEAD) as the data source. The experimental results show that USCNet performs significantly better than U-Net and SegNet networks on this dataset, with significant improvements in metrics such as Mean (μ), Variance (δ), Precision (P), and Recall (R), as well as significantly improved picking performance. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Seismic waves

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Design of High-Performance Terahertz Photoconductive Detectors Based on MSM Metasurfaces 11

Design of High-Performance Terahertz Photoconductive Detecto...

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Infrared, Millimeter-Wave, and Terahertz Technologies XI 2024

作者： Zhang, Junyao Wu, Yiwen Zhang, Ming School of Information and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

ISBN: (数字)9781510682238

ISBN: (纸本)9781510682221

Terahertz wave has been widely used in medical imaging, sensing, and communication. The terahertz (THz) detector is an effective means to obtain information and images. Meanwhile, high-sensitivity THz detectors are necessary for detection and communication systems. However, most of the previously proposed terahertz detectors use butterfly or dipole antennas, which have low responsivity and narrow response bandwidth, limiting the detection of continuous and weak signals. In this work, a photoconductive detector based on metasurface is proposed. In order to improve the terahertz absorption effect of the detector, a metal-semiconductor-metal (MSM) unit array structure is employed. Gallium arsenide (GaAs) has the advantages of direct band gap (1.42 eV), high photo-electric conversion rate and high electron mobility, making it an ideal building block for the preparation of photodetectors. The influence of different structures on the light absorption intensity of the detector is simulated by the finite difference time domain (FDTD) method. The optimized structure achieves absorption of over 99.9% in the corresponding band. Therefore, theoretically determining the metasurface structure can enhance the light absorption intensity of the detector, and provide support for optimizing the structural parameters and realizing a broadband terahertz detector. The current and voltage response of the designed photoconductive detector under given conditions were modeled and analyzed by SILVACO. Among them, the responsivity of the device can reach 0.36 A/W at 0.3V bias voltage and 0.9mW incident light power. The designed metasurface and detector have simple structure and easy fabrication. This design provides a new idea and technical method for the design of terahertz detector. © 2024 SPIE.

关键词： Terahertz wave detectors

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