Titlebook: Advances in Signal Processing and Intelligent Recognition Systems; 6th International Sy Sabu M. Thampi,Sri Krishnan,Jagadeesh Kannan R. Con

年迈

书目名称Advances in Signal Processing and Intelligent Recognition Systems影响因子(影响力)




书目名称Advances in Signal Processing and Intelligent Recognition Systems影响因子(影响力)学科排名




书目名称Advances in Signal Processing and Intelligent Recognition Systems网络公开度




书目名称Advances in Signal Processing and Intelligent Recognition Systems网络公开度学科排名




书目名称Advances in Signal Processing and Intelligent Recognition Systems被引频次




书目名称Advances in Signal Processing and Intelligent Recognition Systems被引频次学科排名




书目名称Advances in Signal Processing and Intelligent Recognition Systems年度引用




书目名称Advances in Signal Processing and Intelligent Recognition Systems年度引用学科排名




书目名称Advances in Signal Processing and Intelligent Recognition Systems读者反馈




书目名称Advances in Signal Processing and Intelligent Recognition Systems读者反馈学科排名

Callus

Offline Signature Verification Based on Spatial Pyramid Image Representation with Taylor Serieses by extending the sobel operators to compute the higher order derivatives of TSE. Our approach captures both local and global features from the signature. We have used weighted histograms. The weight associated with the histogram is directly proportional to the depth of the level. The Support Vect

Intractable

盟军

Performance Improvements in Quantization Aware Training and Appreciation of Low Precision Computatio in 8-bit setting and its performance improvements over the contemporary quantization techniques. We have exhibited our achievement of better and minimized quantization loss, inference time, memory footprint in a LENET on MNIST, CIFAR-10 datasets and MobileNet Architecture on ImageNet dataset. In th

voluble

Analysis of Unintelligible Speech for MLLR and MAP-Based Speaker Adaptationand was less effortful for training compared to a Speaker Dependent (SD) recognizer. Testing of the system was conducted with the UA-Speech Database and the combined algorithm produced improvements in recognition accuracy from 43% to 90% for medium to highly impaired speakers revealing its applicabi

Cougar

Supervised Feature Learning for Music Recommendationodel generates sensible, diverse and personalized recommendations and is effective even on small datasets. We compare our results quantitatively against that of the popular latent factor models for music recommendation and show that our song to vector model outperforms traditional recommendation met

LUMEN

gait-cycle

Periocular Recognition Under Unconstrained Image Capture Distancesn methods improve the resolution of images alike without taking the capture range into account and hence are not quality driven. In order to improve the recognition rate irrespective of the acquisition distance, we propose to make use of transfer learning. The novelty of our approach is that it is t

abreast

Spongy-Bone

Jianwen Li,Moshe Y. Vardi,Kristin Y. Rozierthe models. For this work, we have considered variants of convolutional neural networks. Experimental works have performed on two different datasets and for both datasets, the results of the fine-tuned network outperforms all other approaches.