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38th International Conference on VLSI Design, VLSID 2025

作者： Saha, Kritanta Banerjee, Pritha Sur-Kolay, Susmita Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India Sister Nivedita University Department of Computer Science and Engineering Kolkata India University of Calcutta Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331522445

Given n points in the 2D plane which are the pins of a net, a Rectilinear Steiner Minimum Tree (RSMT) which consists only of vertical and horizontal line segments is the shortest network to interconnect the pins along with a few extra (Steiner) points. An RSMT for a net has a minimum total wirelength in Manhattan distance and acts as a backbone to many state-of-the-art global routers. However, finding an RSMT is NPcomplete. In this work, we propose a method for constructing an RSMT with an image-to-image translation using a conditional generative adversarial network (cGAN) based model. Images of RSMTs with optimal wirelength for randomly generated nets form our training dataset. Experimental results show that, after training, our model generates RSMTs with = 0.348% wirelength error on average compared with the optimal solution for nets with degrees up to 50. This is the first successful attempt to solve the RSMT construction problem as an image-to-image translation problem using a cGAN based model. © 2025 IEEE.

关键词： Generative adversarial networks

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Stable switching behavior of low-temperature ZrO_(2)RRAM devices realized by combustion synthesis-assisted photopatterning

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Journal of Materials science & Technology 2024年第22期189卷 68-76页

作者： Bongho Jang Junil Kim Jieun Lee Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaegu 41566Korea School of Electronics Engineering Kyungpook National UniversityDaegu 41566Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988Korea

We have realized efficient photopatterning and high-quality ZrO_(2)films through combustion synthesis and manufactured resistive random access memory(RRAM)devices with excellent switching stability at low temperatures(250℃)using these *** synthesis reduces the energy required for oxide conversion,thus accelerating the decomposition of organic ligands in the UV-exposed area,and promoting the formation of metal-oxygen networks,contributing to *** analysis confirmed a reduction in the conversion temperature of combustion precursors,and the prepared combustion ZrO_(2)films exhibited a high proportion of metal-oxygen bonding that constitutes the oxide lattice,along with an amorphous ***,the synergistic effect of combustion synthesis and UV/O_(3)-assisted photochemical activation resulted in patterned ZrO_(2)films forming even more complete metal-oxygen *** devices fabricated with patterned ZrO_(2)films using combustion synthesis exhibited excellent switching characteristics,including a narrow resistance distribution,endurance of 103 cycles,and retention for 105 s at 85℃,despite low-temperature *** synthesis not only enables the formation of high-quality metal oxide films with low external energy but also facilitates improved photopatterning.

关键词： ZrO_(2) Combustion Sol-gel RRAM Patterning

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Microplastic pollution in the German aquatic environment: Existence, interactions and research needs

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Environmental engineering Research 2025年第4期30卷 129-155页

作者： Shrivastava, Vikalp Silori, Rahul Verma, Soumya Nandan, Abhishek Kumar, Sanjeev Shekher Giri, Balendu Natural science Technische Hochschule Bingen Rhineland-Palatinate Bingen55411 Germany Sustainability Cluster School of Advanced Engineering UPES Uttarakhand Dehradun248007 India School of Computer Science UPES Uttarakhand Dehradun248007 India

Plastics, once celebrated for their versatility, have now become a significant environmental threat due to their persistence, with microplastics (MPs) posing serious concerns. This study examines the prevalence of MPs across various environmental domains, focusing on their impact on ecosystems and human health, along with analytical procedures and treatment techniques. It has been found that Fourier Transform Infrared Spectroscopy (FTIR) is the most common analytical procedure for MPs detection. Few policies/steps have been formulated by international organizations/governments to impede the proliferation of MPs, which have been duly included herein. Germany, as Europe's largest economy and a major contributor to plastic pollution, surprisingly has a dearth of research in this area. So, this study focuses on MP pollution in the German aquatic environment and highlights alarming concentrations of MPs, with up to 11,050 particles/L in rivers and 9,000 particles/m³ in wastewater treatment plants (WWTPs). Fibers dominate in shape, and polyethylene (PE), polypropylene (PP), and polystyrene (PS) are the most common polymers, primarily originating from municipal/industrial wastewater. Based on these findings and identified gaps, the paper provides recommendations for future research and areas of improvement, such as standardized measurement units, more studies, and combined effects of MPs with other emerging contaminants. © 2025, Korean Society of Environmental Engineers. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Quantifying Racial Segregation Through Continuous-Time Quantum Walks 27th

Quantifying Racial Segregation Through Continuous-Time Quant...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Yutong Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China Xi’an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9783031781919

With the development of cities, racial segregation is one of the reasons for social inequality on a large scale, it is also a major factor affecting economic development. However, racial segregation has received comparatively limited attention in research. In the paper, we propose a segregation index independent of any parameters( titled Quantum Walk Convergence Time), which calculates the convergence time statistics for accessing different racial categories through quantum walks on complex networks constructed from urban systems. The magnitude of the time statistics represents the degree of racial segregation. The results generated by our method can also be applied to other social factors, including family situation, income level, and education level. We evaluate our method using large-scale real datasets, including statistics from the U.S. Census Bureau and the Office for National Statistics in the UK. The results demonstrate the close correlation between our method and the degree of racial segregation, showing advantages over traditional random walk methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Continuous time systems

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advanced U-Net++ Architecture for Precise Brain Tumor Segmentation in MRI Images: A Robust Solution for Medical Image Analysis

Advanced U-Net++ Architecture for Precise Brain Tumor Segmen...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham Department of Computer Science and Engineering India University of North Texas Department of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350385199

Tumor segmentation in MRI images is a crucial process in medical imaging, aimed at accurately identifying and delineating tumor regions within the brain or other tissues. Hence proposed a modified U-Net++ based segmentation with predicted mask. To further improve the performance of the proposed method, incorporated additional layers into the U Net++ architecture. U-Net++ consists of Encoder Decoder and the encoder captures image content, while the decoder delineates object boundaries. U-Net++ excels in capturing both high-level and intricate details, which significantly boosts segmentation performance. This helps in accurate tumor segmentation of complex MRI images. To measure the effectiveness of the proposed model for segmentation and detection, Accuracy, Intersection over Union(IOU), Dice Coefficient and Area under the ROC Curve(AUC) scores are used. The proposed U-Net++ brought accuracy to 99 percent, which could significantly help in the process of accurate segmentation of human tissues. Accuracy of 39 % for IOU of 80, Dice Coefficient of 89%, and 33% for the test set. This is equivalent to 8% sensitivity and accuracy and AUC of 78. The proposed method can be used as an additional diagnosis aid to help doctors determine the suitability of the treatment and the changes occurring in the body during the development of the disease © 2024 IEEE.

关键词： Magnetic resonance imaging

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Deep Learning based Target detection and Recognition using YOLO V5 algorithms from UAVs surveillance feeds 2

Deep Learning based Target detection and Recognition using Y...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Kumar, Sushil Kumar, C.R.S. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

This study aims to show how the most recent object identification algorithms (YOLO V5 and YOLO V7) may be used to recognise targets in a real-world setting using surveillance feed collected by UAVs/*** Russia-Ukraine conflict provided UAV imagery that was gathered and processed to better understand how object identification might result in deducing vital inputs for rapid and timely decision making in a warlike *** on the video was performed using the best and most accurate models from YOLOV5 which was obtained from open-source UAVs *** of its superior OD capabilities, unmanned aerial vehicles (UAVs) are essential to the realisation of any robot's full *** the YOLO V5 model was trained for a greater total number of epochs to substantially improve the object class detection *** analysing the Confusion matrix and the performance hyperparameters of the algorithm, this study compiles the performance metrics to make conclusions and optimise outcomes. © 2023 IEEE.

关键词： Antennas

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Machine Learning Based Approach to Disinformation Detection Using Twitter Data 2

Machine Learning Based Approach to Disinformation Detection ...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Yadav, Sanjeev Kumar, C.R.S. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

Fake news detection on social media has assumed enhanced importance and is a trending research topics for data analysts around the world. While most of fake news may be aimed at commercial or personal gains, some of it may cause deliberate harm to a nation's progress. Twitter, as part of social media platforms (SMPs) is a leading portal being used by everyone for gaining access and giving out information. Leading the spread of disinformation propagation via fake news, twitter has emerged as one of the leading SMPs. The sheer quantum of tweets makes it very challenging to sort the news and remove anomalies. Various machine learning techniques have been devised to identify fake tweets. This report aims at studying prominent machine learning techniques for detection of disinformation propagation on Twitter. Generally, researchers have identified detection of fake tweets as a classification problem but some unsupervised and unconventional learning models are also available. Major effort for any supervised learning technique is creation of data corpus. The paper highlights some techniques for the same. Consequently, inclusion of context as part of feature extraction has been achieved to align the intent behind the tweet with the text. As a first step towards the solution, a sentiment analysis of tweets from a set of users has been recommended details have been illustrated in subsequent sections. Various context based technique have been studied and implemented on data corpus and results produced have been compared. The report also studies various NLP techniques for data pre-processing and feature extraction before using classifiers to draw the outcome. © 2023 IEEE.

关键词： Feature extraction

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Object Detection Algorithm based on Structural Modification in the Convolutional Neural Network for Remote Sensing Satellite İmages 8

Object Detection Algorithm based on Structural Modification ...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Singh, Abhimanyu Nene, Manisha J Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9798350334012

for machine vision to make sense of an image, object recognition techniques must first locate all of the items of interest within the frame of reference. Several strategies have been proposed to address this issue, many of which draw inspiration from computer vision and deep learning techniques. Existing methods, however, consistently underperform when trying to recognise small, dense objects and often fail altogether when trying to detect objects that have undergone random geometric alterations. In this research, we examine and compare state-of-the-art object detection techniques, then suggest a tweaked convolutional object detection network to address the problems with the status *** investigation shows superior performance than current best practices. For this purpose, we employ deep convolutional networks to collect multi-scaled features, and we tweak the convolutional structures to account for geo-metric transformations.. © 2023 IEEE.

关键词： Object detection

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GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer's Classification

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer'...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

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