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Microwave Acoustics and Mechanics (IC-MAM),IEEE MTT-S International Conference on

作者： Menghui Li Mengke Qi Yuanhang Chen Yimim Cheng Liang Cao Xiaojing Mu The Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education Chongqing University International Research and Development Center of Micro-Nano Systems and New Materials Technology Chongqing University Chongqing China

ISBN: (数字)9798350331462

ISBN: (纸本)9798350331479

This work introduces a new method for suppressing transverse modes, which is achieved by reducing the SAW velocity at the end of the IDT electrodes through selective filling with SiO 2. And the dimensional parameters of the selected positions were optimized to suppress transverse modes in the passband without degrading their performance.

关键词： Silicon compounds Electrodes Surface acoustic waves Filling Finite element analysis Passband Resonators

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Deep Learning for Accurate Histopathological Classification of Lung and Colon Cancer Using CNN and EfficientNetB3 Models

Deep Learning for Accurate Histopathological Classification ...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Eshika Jain Savinder Kaur Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This research work suggests a quick way to sort histopathological pictures of lung and colon cancers by using two deep learning models: a custom CNN and EfficientNetB3. In this respect, the proposed models were trained using a large dataset of both cancerous and benign tissues and tested for accuracy, precision, recall, and F1-score. The CNN model showed an overall accuracy of $\mathbf{9 9 \%}$, demonstrating its capability in differentiating between various types of cancers and benign conditions. EfficientNetB3 further optimized the performance, yielding $\mathbf{9 8 \%}$ accuracy with well-balanced metrics for all the classes with a high score in recognizing lung and colon adenocarcinoma and benign tissues. Robustness and good generalization on unseen data are further indicative of their potential clinical diagnostic utility. Steps for the future: rank pruning of slight misclassifications and application extension to other cancers than those under consideration by incorporating additional data and using advanced techniques, including transfer learning for real-time diagnostic support and detection at earlier cancer stages in healthcare.

关键词： Deep learning Accuracy Squamous cell carcinoma Lungs Transfer learning Colorectal cancer Real-time systems Robustness Convolutional neural networks Cancer

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Analysis of Traffic Patterns and Optimization of Routing for Cognitive Radio Networks 15

Analysis of Traffic Patterns and Optimization of Routing for...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jayashankari, J. Bhushan, Bharat Rekha, M.M. Bagade, Jayashree V. Yuvaraj, K. Garg, Prateek Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Department of Chemistry and Biochemistry Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

Cognitive radio networks (CRNs) had been developed to satisfy the demands of contemporary communiqué structures. CRNs are characterized by way of their adaptability to dynamically change their operational parameters and configurations for you to meet the converting wishes of customers. To do that, CRNs must be able to investigate the cutting-edge visitors styles to be able to optimize their routing based on the to be had resources inside the device. This paper offers a observe of visitors patterns and an optimization of routing for CRNs. We analyze the community site visitor's styles and use the patterns alongside the network's resource optimization methods to generate most advantageous routes on the way to maximize throughput and limit put off. Via simulations, we evaluate the overall performance of our proposed gadget and examine it to other existing routing procedures. The simulation outcomes display that our machine achieves an improvement of 7-14% in throughput. Additionally, our proposed gadget reduces the average cease-to-cease put off by way of 10-20%. These effects suggest that our proposed system is an effective technique to routing in CRNs. © 2024 IEEE.

关键词： Cognitive radio

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Record Real-time 2Pbit/s·km Transmission over 20km Hollow Core Fiber Using Widened C+L 12THz Bandwidth and 1.2Tbit/s Transponder with Symbol Rate of 135GBaud

Record Real-time 2Pbit/s·km Transmission over 20km Hollow C...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Feng, Lipeng Liu, Hong Chang, Kun Luo, Jie Zhang, Anxu Yan, Baoluo Lu, Nan Huo, Xiaoli Shi, Hu Wang, Xishuo Gao, Xia Liu, Yuyang Ma, Tao Li, Junjie Zhang, Chengliang China Telecom Research Institute State Key Laboratory of Optical Fiber and Cable Manufacture Technology China Telecom Research Institute Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Yangtze Optical Fibre and Cable Joint Stock Limited Company Wuhan China Cloud Network Development Department China Telecom Co. Ltd. Beijing China

We successfully demonstrate a real-time transmission over 20km hollow core fiber (HCF). With widened C+L 12THz bandwidth and highest bit rate per wavelength of 1.2Tbit/s, record real-time capacity of 100.4Tbit/s and c... 详细信息

ISBN: (纸本)9798350379266

关键词： hollow core fiber real-time transmission widened C+L bandwidth

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development of a dual targeting scaffold of SET7/MLL inhibitor for castration-resistant prostate cancer treatment

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Genes & Diseases 2023年第6期10卷 2260-2262页

作者： Guodong Li Qi Huang Vincent Kam Wai Wong Wanhe Wang Chung-Hang Leung Institute of Chinese Medical Sciences and State Key Laboratory of Quality Research in Chinese Medicine University of MacaoMacao SAR 999078China Dr.Neher's Biophysics Laboratory for Innovative Drug Discovery State Key Laboratory of Quality Research in Chinese MedicineMacao University of Science and TechnologyMacao SAR 999078China Institute of Medical Research Northwestern Polytechnical UniversityXi'anShaanxi 710072China Macao Centre for Research and Development in Chinese Medicine Institute of Chinese Medical SciencesUniversity of MacaoMacao SAR 999078China Department of Biomedical Sciences Faculty of Health SciencesUniversity of MacaoMacao SAR 999078China Zhuhai UM Science and Technology Research Institute ZhuhaiGuangdong 519000China

Histone methyltransferase enzyme SET7 and mixed-lineage leukemia protein(MLL)complex are crucial co-activators of androgen receptor(AR)and have recently emerged as potential therapeutic targets for advanced castration-resistant prostate cancer(CRPC).In this study,we described the identification of a rhodium-based hybrid complex(SM_1)as a potent blocker of AR activity via simultaneously inhibiting SET7 and MLL complex activity,which makes it a potential lead scaffold for CPRC drug development.

关键词： cancer inhibiting treatment

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The Effect of Modified Styrene Butadiene Rubber Latex on the Properties of Emulsified Asphalt and Mixture 10th

The Effect of Modified Styrene Butadiene Rubber Latex on the...

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10th International Conference on Civil Engineering, ICCE2023

作者： Zhou, Qiwei Shi, Zhixin Zheng, Shilun Zhao, Mengzhen Yuan, Mingyuan Xing, Chunzhuang Xu, Fu China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing China Chongqing Jiaotong University Chongqing China Zunyi Highway Administration Bureau Guizhou Zunyi China Guangxi Cenxing Expressway Development Co. Ltd. Guangxi China Chongqing Zhixiang Paving Technology Engineering Co. Ltd. Chongqing China

ISBN: (纸本)9789819743544

In order to study the effect of styrene butadiene rubber (SBR) latex at various dosages on the properties of emulsified asphalt and its mixtures as well as to reveal SBR’s modification mechanism and action, the conventional test, dynamic shear rheology test, fluorescence microscopy test, infrared spectroscopy test, contact angle test, wet wheel abrasion test and rutting deformation test were carried out to investigate the conventional properties, rheological properties, microphase structure, adhesive properties and the abrasion resistance of its mixtures, the resistance to water damage and rutting resistance of the modified SBR emulsified asphalt. The results show that SBR can make a significant improvement in ductility and softening point of emulsified asphalt with reduction in emulsified asphalt’s penetration. SBR emulsified asphalt complex shear modulus and rutting factor increases and the phase angle decreases with the dosage of SBR The spatial structure of SBR can be stabilized in the emulsified asphalt, the linear correlation coefficients between the SBR doping and its area share were found to be high by binarized fluorescence microscopy image analysis. The SBR can make improvement in the contact angle for emulsified asphalt and in free energy on emulsified asphalt’s surface. The physical modification between SBR and emulsified asphalt. And SBR features excellent road properties, such as abrasion resistance, water damage resistance and rutting resistance. © The Author(s) 2024.

关键词： Fluorescence microscopy

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A Way to Integrate ML Algorithm with the NN for the Purpose of Crop Production Through Continuous Monitoring 4

A Way to Integrate ML Algorithm with the NN for the Purpose ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Varshney, Neeraj Rajeswari, A. Mounika Pramanik, Atreyi Alabdeli, Haider Kumar, Pranav Prashanth Kumar, K. Gla University Department of Computer Engineering and Applications Mathura India Cmr College of Engineering & Technology Department of Cse India Uttaranchal University School of Applied and Life Sciences Division of Research and Innovation Uttarakhand Dehradun India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Department of Civil Engineering Karnataka Karkala India

ISBN: (纸本)9798350360165

This check examines the evolution of machine studying (ML) strategies in forecasting hydrological droughts, focusing at the Standardized Runoff Index (SRI) in the Han River basin, South Korea. Given the dramatic climatic shifts witnessed throughout this era, our research evaluates the general performance of six ML models - Decision Trees (DT), Support Vector Machines (SVM), Deep Learning Neural Networks (DLNN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), and Fuzzy Rule-Based Systems (FRBS) - in predicting drought severity and frequency. Emphasis is placed on the model and refinement of these fashions to account for converting climatic styles and their impact on drought characteristics. The DT version, renowned for its predictive accuracy and computational performance, is identified as specifically promising. This has a look at targets to manual water resource manage and disaster preparedness via advanced drought forecasting methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

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Harnessing EfficientNetB5 for Accurate Skin Disease Classification: A Deep Learning Approach 3

Harnessing EfficientNetB5 for Accurate Skin Disease Classifi...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kaushik, Pratham Sharma, Pooja Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India

ISBN: (纸本)9798331542375

Skin diseases are best treated when detected at a very early stage and deep learning can be used to help in this identification process. In this work, an EfficientNet deep learning model is introduced to classify 10 types of skin diseases, such as Melanoma, Basal Cell Carcinoma, and Psoriasis. The proposed model has an overall accuracy of 87%, which shows high precision and recall for conditions such as Melanoma and Melanocytic Nevi (NV). Analysis of performance through a classification report and confusion matrix shows that although the model is accurate, there is still a problem in differentiating between Psoriasis & Lichen Planus and Seborrheic Keratoses. The results show that EfficientNet can be effectively applied for dermatological purposes and provide an effective approach to automated skin disease diagnosis. Some potential future work could enhance the model's capacity to distinguish between visually similar diseases and its applicability in clinical practice. © 2025 IEEE.

关键词： Deep learning

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Performance analysis and optimization of free cooling strategies for a liquid-cooled data center

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Building Simulation 2023年第8期16卷 1317-1330页

作者： Weinan Zhou Qin Sun Weimin Luo Wei Xiao Pengfei Cui Wei Wu Kaijun Dong Guangzhou Institute of Energy Conversion Chinese Academy of SciencesGuangzhouChina University of Chinese Academy of Sciences BeijingChina Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development GuangzhouChina Guangdong Fushengda Intelligent Technology Co. Ltd.Dongguan 523770China Guangzhou Goaland Energy Conservation Tech.Co. Ltd.Guangzhou 510663China School of Energy and Environment City University of Hong KongHong KongChina

The increasing power density of IT electronics and the enormous energy consumption of data centers lead to the urgent demand for efficient cooling *** to its efficiency and safety,liquid-cooled heat sink technology may gradually replace air-cooled technology over *** the ambient or higher water supply temperature,the liquid-cooled technology shortens the operating time of the chiller and improves its coefficient of performance,while the pump power consumption may increase for satisfying the constant cooling ***,it is significant to study the optimal water supply temperature to achieve energy-efficient operation of data centers.A virtual 30.1 kW data center is considered as the case,the liquid-cooled system is constructed with a combination of innovative manifold microchannel heat sink with oblique fins and indirect evaporative cooling technology to minimize energy consumption.A hybrid thermal management model integrating the heat dissipation model and the power consumption model is established by TRNSYS and FLUENT *** the highest chip-safe operating temperature premise,the energy performance is analyzed under various water supply temperatures in *** result shows that only 21.5-hour mechanical cooling is needed with the 30℃server inlet temperature throughout the *** the minimized power consumption occurs with the constant 29℃server inlet ***,the temperature adaptive control strategy(TACS)is adopted to test the cooling system power consumption under different regulation frequencies,and the by-week TACS can achieve another 11.5%energy saving than the minimum power consumption of the constant temperature control strategy.

关键词： data center liquid cooling system manifold microchannel heat sink indirect evaporative cooling control strategy

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Hybrid Deep Learning for Multi-Modal Image Fusion: Elevating Brain Tumor Detection

Hybrid Deep Learning for Multi-Modal Image Fusion: Elevating...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Shiva Mehta Amanveer Singh Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

This work presents a new dual deep learning framework that incorporates CNNs and ViTs into the multi-modal medical picture fusion to enhance the diagnostic accuracy of the diagnosed brain tumour. This integration concerns MRI and PET information, applying the ability of CNNs to handle local spatial information and ViTs in collecting long-range relationships. Here, to test the efficacy of the proposed model, a publicly available multi-modal brain tumour database was used, where significant performance improvements were achieved over the conventional techniques. The developed model achieved a sensitivity of 96.8%, while the traditional CNN-based methods attained a sensitivity of only 91.2%. In addition, the research used it to achieve a high accuracy rate of 95.7 percent, a recall rate of 95.1 percent, and an F1 score of 95.4 percent for infallibility in locating tumors with minimal false positives and negatives. It was realized that the model in this study provided improved picture quality, with an SSIM of 0.89 compared to the 0.82 of earlier fusion techniques. The pilot qualitative study confirmed that the work of the fused pictures showed a better combination of PET and MRI information by merging spatial features with metabolic data to help distinguish the tumour's boundaries more clearly and identify small or irregular form tumours. The proposed CNN-ViT model outperforms other methods for detecting tumors with high levels of accuracy and high-quality images that give medicine a vital tool. The methodology has high potential to be applied to other medical imaging jobs as well.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Brain tumors Brain modeling Transformers Medical diagnostic imaging Image fusion Diseases

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