2019 National Conference on Communications (NCC)最新文献_第9页

LSTM-Deep Neural Networks based Predistortion Linearizer for High Power Amplifiers 基于lstm深度神经网络的高功率放大器预失真线性化器

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732178

Deepmala Phartiyal, M. Rawat

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引用次数: 13

Splitting Merged Characters of Kannada Benchmark Dataset using Simplified Paired-Valleys and L-Cut 基于简化成对谷和l -切的卡纳达语基准数据集合并字符分割

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732239

H. Kumar, A. Madhavaraj, A. Ramakrishnan

引用次数: 4

Slot Cut Modified Triangular Shape Microstrip Antenna for Circular Polarization 圆极化槽切改进三角形微带天线

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732180

Akshay Doshi, A. Deshmukh, S. Deshmukh, K. P. Ray

引用次数: 2

Predistortion Linearizer Design for Ku Band RF Power Amplifier Ku波段射频功率放大器的预失真线性器设计

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732215

Girish Chandra Tripathi, M. Rawat

引用次数: 2

Development of Assamese Text-to-speech System using Deep Neural Network 基于深度神经网络的阿萨姆语文本转语音系统的开发

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732262

A. Deka, Priyankoo Sarmah, K. Samudravijaya, S. Prasanna

引用次数: 8

Towards the Exact Rate Memory Tradeoff in Coded Caching 在编码缓存中实现精确的速率内存权衡

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732186

K. Vijith, B. K. Rai, T. Jacob

引用次数: 8

Brain Tumor Segmentation Using Discriminator Loss 基于鉴别器损失的脑肿瘤分割

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732196

Joydeep Das, Rashmin Patel, Vinod Pankajakshan

{"title":"Brain Tumor Segmentation Using Discriminator Loss","authors":"Joydeep Das, Rashmin Patel, Vinod Pankajakshan","doi":"10.1109/NCC.2019.8732196","DOIUrl":"https://doi.org/10.1109/NCC.2019.8732196","url":null,"abstract":"The emerging field of Computer Vision has found enormous applications in our day-to-day lives and Medical Image Processing is one of the most prominent fields among them. Brain Tumor Segmentation is an important and challenging task because of the variety in shapes, sizes and texture content of the various types of brain tumors. Specifically, MICCAI BraTS organizes Brain Tumor Segmentation challenge every year. Since the evolution of CNNs it has obtained state-of-the-art results in the majority of computer vision related tasks. On BraTS Challenge 2017, an assemble average of various CNN models (EMMA) holds the state-of-the-art performance. In this paper, we have proposed a model inspired by the classic Generative Adversarial Network (GAN). The proposed network has two models namely, Generator or Segmentor which generates label map of the input image and a Discriminator which helps the Generator model for an optimum solution by taking into account both short as well as long-distance spatial correlations between pixels with the help of a novel multi-scale loss function. The proposed architecture has three GANs in a cascaded fashion, each for Whole Tumor, Tumor Core and Enhancing Tumor, where the former network helps in effective reduction of false positives for the later networks. Our method also employs a multi-scale loss function derived from intermediate layers of Discriminator rather than depending just on a final layer cross-entropy loss. A mutli-scale loss function also reduces unnecessary smoothing on contours. The proposed method performed comparatively better than the state-of-the-art techniques, having Dice scores of 0.820, 0.874 and 0.783 for Enhancing Tumor, Whole Tumor and Tumor Core respectively.","PeriodicalId":6870,"journal":{"name":"2019 National Conference on Communications (NCC)","volume":"12 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74277603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Graph Based Clustering and Preconditioning of V-MIMO Wireless Sensor Networks 基于图的V-MIMO无线传感器网络聚类与预处理

2019 National Conference on Communications (NCC) Pub Date : 2019-02-01 DOI: 10.1109/NCC.2019.8732213

Rakesh Mundlamuri, B. Thangapandian, Vijay Kumar Chakka, Srikanth Goli

引用次数: 0

Maximally Recoverable Codes with Hierarchical Locality 具有层次局部性的最大可恢复码

2019 National Conference on Communications (NCC) Pub Date : 2019-01-09 DOI: 10.1109/NCC.2019.8732208

Aaditya M. Nair, Lalitha Vadlamani

引用次数: 6

Adaptive Polarization Control for Coherent Optical Links with Polarization Multiplexed Carrier 偏振复用载波相干光链路的自适应极化控制

2019 National Conference on Communications (NCC) Pub Date : 2018-05-06 DOI: 10.1109/NCC.2019.8732190

Mehul Anghan, Rashmi Kamran, Nihir Gulati, N. Nambath, Shalabh Gupta

引用次数: 5