Page 8 - Task Analysis News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Stay updated with breaking news from Task analysis. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

Top News In Task Analysis Today - Breaking & Trending Today

"Learning to Charge RF-Energy Harvesting Devices in WiFi Networks" by Yizhou Luo and Kwan Wu Chin

Future WiFi networks will be powered by renewable sources. They will also have radio frequency (RF)-energy harvesting devices. In these networks, a solar-powered access point (AP) will be tasked with supporting both nonenergy harvesting or legacy data users such as laptops, and RF-energy harvesting sensor devices. A key issue is ensuring the AP uses its harvested energy efficiently. To this end, this article contributes two novel solutions that allow the AP to control its transmit power to meet the data rate requirement of legacy users and also to ensure RF-energy devices harvest sufficient energy to transmit their sensed data. Advantageously, these solutions can be deployed in current wireless networks, and they do not require perfect channel gain information to sensor devices or noncausal energy arrivals at an AP. The first solution uses a deep Q-network (DQN) whilst the second solution uses model predictive control (MPC) to manage the AP’s transmit power subject to its available e ....

Receding Horizon Control , Einforcement Learning Rl , Signal To Noise Ratio , Task Analysis , Wireless Fidelity , கேளுங்கள் பகுப்பாய்வு ,

"SA-LuT-Nets: Learning Sample-adaptive Intensity Lookup Tables for Brai" by Biting Yu, Luping Zhou et al.

Abstract
In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity lookup table (LuT) that dynamically transforms the intensity contrast of each input MR image to adapt to the following segmentation task. Specifically, the proposed deep SA-LuT-Net framework consists of a LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific nonlinear intensity mapping function through communication with the segmentat ....

Image Segmentation , Magnetic Resonance Imaging Mri , Neural Network , Solid Modeling , Able Lookup , Task Analysis , Hree Dimensional Displays , கேளுங்கள் பகுப்பாய்வு ,

"Learning Graph Convolutional Networks for Multi-Label Recognition and " by Zhaomin Chen, Xiu Shen Wei et al.

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label- ....

Graph Convolutional Networks , Computational Modeling , Convolutional Neural Networks , Face Recognition , Graph Convolutional Networks , Image Recognition , Abel Dependency , Ulti Label Recognition , Task Analysis , கணக்கீட்டு மாடலிங் , கேளுங்கள் பகுப்பாய்வு ,

"Relation Regularized Scene Graph Generation" by Yuyu Guo, Lianli Gao et al.

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph convolution networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on ....

Feature Extraction , Raph Convolution Networks Gcns , Cene Graph Generation Sgg , Task Analysis , Isual Relationship , கேளுங்கள் பகுப்பாய்வு ,

"Pedestrian Motion Trajectory Prediction in Intelligent Driving from Fa" by Yingfeng Cai, Lei Dai et al.

Abstract
Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent driving from far shot first-person perspective video. To accomplish this task, we proposed a deep learning model for pedestrian motion trajectory prediction from far shot first-person perspective video with four key innovations: a) A macroscopic pedestrian trajectory prediction module is established under the close correlation between neighboring frames to estimate the pedestrian motion track on the whole; b) A relative motion transformation module of vehicle-mounted camera is designed to consider the effect of vehicle-mounted camera s ego-motion on the pedestrian motion track; c) We set up a circular training module to maintain the number of parameters in our model to si ....

Adaptation Models , Auto Motion , Ircular Training , Deep Learning , Legged Locomotion , Edestrian Motion Trajectory Prediction , Predictive Models , Task Analysis , கேளுங்கள் பகுப்பாய்வு ,