scholarly journals Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6929
Author(s):  
Bingshu Wang ◽  
C. L. Philip Chen
Keyword(s):  
State Of The Art ◽  
Computation Time ◽  
Document Image ◽  
Shadow Detection ◽  
Image Size ◽  
Filling Method ◽  
Water Filling ◽  
Retinex Theory ◽  
Water Filling Algorithm ◽  
Local Water

Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

scholarly journals Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

2017 ◽  
Vol 63 (1) ◽  
pp. 79-84
Author(s):  
M. K Noor Shahida ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail
Keyword(s):  
Power Allocation ◽  
Network Architecture ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Downlink Transmission ◽  
Allocation Algorithm ◽  
Mimo Antenna ◽  
Water Filling ◽  
Water Filling Algorithm

Abstract Energy Efficiency (EE) is becoming increasingly important for wireless communications and has caught more attention due to steadily rising energy costs and environmental concerns. Recently, a new network architecture known as Massive Multiple-Input Multiple-Output (MIMO) has been proposed with the remarkable potential to achieve huge gains in EE with simple linear processing. In this paper, a power allocation algorithm is proposed for EE to achieve the optimal EE in Massive MIMO. Based on the simplified expression, we develop a new algorithm to compute the optimal power allocation algorithm and it has been compared with the existing scheme from the previous literature. An improved water filling algorithm is proposed and embedded in the power allocation algorithm to maximize EE and Spectral Efficiency (SE). The numerical analysis of the simulation results indicates an improvement of 40% in EE and 50% in SE at the downlink transmission, compared to the other existing schemes. Furthermore, the results revealed that SE does not influence the EE enhancement after using the proposed algorithm as the number of Massive MIMO antenna at the Base Station (BS) increases.

scholarly journals ДОСЯГНЕННЯ ЕФЕКТИВНОГО РОЗПОДІЛЕНОГО ПЛАНУВАННЯ ЗА ДОПОМОГОЮ ЧЕРГ ПОВІДОМЛЕНЬ У ХМАРІ ДЛЯ БАГАТОЗАДАЧНИХ ОБЧИСЛЕНЬ ТА ВИСОКОПРОДУКТИВНИХ ОБЧИСЛЕНЬ

2020 ◽  
Author(s):  
Старовойтенко Олексій Володимирович
Keyword(s):  
Distributed Systems ◽  
System Performance ◽  
State Of The Art ◽  
Computation Time ◽  
Cloud Services ◽  
Distributed Scheduling ◽  
Message Processing ◽  
Computational Problem ◽  
Control Units ◽  
And Control

Due to the growth of data and the number of computational tasks, it is necessary to ensure the required level of system performance. Performance can be achieved by scaling the system horizontally / vertically, but even increasing the amount of computing resources does not solve all the problems. For example, a complex computational problem should be decomposed into smaller subtasks, the computation time of which is much shorter. However, the number of such tasks may be constantly increasing, due to which the processing on the services is delayed or even certain messages will not be processed. In many cases, message processing should be coordinated, for example, message A should be processed only after messages B and C. Given the problems of processing a large number of subtasks, we aim in this work - to design a mechanism for effective distributed scheduling through message queues. As services we will choose cloud services Amazon Webservices such as Amazon EC2, SQS and DynamoDB. Our FlexQueue solution can compete with state-of-the-art systems such as Sparrow and MATRIX. Distributed systems are quite complex and require complex algorithms and control units, so the solution of this problem requires detailed research.

A Novel Non-Decreasing Temperature Based Simulated Annealing for Flow Shop Problems

2015 ◽  
Vol 764-765 ◽  
pp. 1390-1394
Author(s):  
Ruey Maw Chen ◽  
Frode Eika Sandnes
Keyword(s):  
Simulated Annealing ◽  
Flow Shop ◽  
State Of The Art ◽  
Computation Time ◽  
Search Space ◽  
Control Mechanisms ◽  
Flow Shop Problem ◽  
Simple Implementation ◽  
Two Phases ◽  
Permutation Schedule

The permutation flow shop problem (PFSP) is an NP-hard permutation sequencing scheduling problem, many meta-heuristics based schemes have been proposed for finding near optimal solutions. A simple insertion simulated annealing (SISA) scheme consisting of two phases is proposed for solving PFSP. First, to reduce the complexity, a simple insertion local search is conducted for constructing the solution. Second, to ensure continuous exploration in the search space, two non-decreasing temperature control mechanisms named Heating SA and Steady SA are introduced in a simulated annealing (SA) procedure. The Heating SA increases the exploration search ability and the Steady SA enhances the exploitation search ability. The most important feature of SISA is its simple implementation and low computation time complexity. Experimental results are compared with other state-of-the-art algorithms and reveal that SISA is able to efficiently yield good permutation schedule.

scholarly journals Robust Waveform Design Based on Bisection and Maximum Marginal Allocation Methods with the Concept of Information Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Bin Wang ◽  
Xiaolei Hao
Keyword(s):  
Mutual Information ◽  
Optimization Design ◽  
Waveform Design ◽  
Observation Time ◽  
Cognitive Radar ◽  
Bisection Algorithm ◽  
Water Filling ◽  
Water Filling Algorithm ◽  
Concept Of Information ◽  
Transmitted Waveform

Cognitive radar can overcome the shortcomings of traditional radars that are difficult to adapt to complex environments and adaptively adjust the transmitted waveform through closed-loop feedback. The optimization design of the transmitted waveform is a very important issue in the research of cognitive radar. Most of the previous studies on waveform design assume that the prior information of the target spectrum is completely known, but actually the target in the real scene is uncertain. In order to simulate this situation, this paper uses a robust waveform design scheme based on signal-to-interference-plus-noise ratio (SINR) and mutual information (MI). After setting up the signal model, the SINR and MI between target and echo are derived based on the information theory, and robust models for MI and SINR are established. Next, the MI and SINR are maximized by using the maximum marginal allocation (MMA) algorithm and the water-filling method which is improved by bisection algorithm. Simulation results show that, under the most unfavorable conditions, the robust transmitted waveform has better performance than other waveforms in the improvement degree of SINR and MI. By comparing the robust transmitted waveform based on SINR criterion and MI criterion, the influence on the variation trend of SINR and MI is explored, and the range of critical value of Ty is found. The longer the echo observation time is, the better the performance of the SINR-based transmitted waveform over the MI-based transmitted waveform is. For the mutual information between the target and the echo, the performance of the MMA algorithm is better than the improved water-filling algorithm.

scholarly journals Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering

2019 ◽  
Vol 33 ◽  
pp. 8658-8665 ◽  
Author(s):  
Xiangpeng Li ◽  
Jingkuan Song ◽  
Lianli Gao ◽  
Xianglong Liu ◽  
Wenbing Huang ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Computation Time ◽  
Comparable Result ◽  
Video Encoding ◽  
Visual Question Answering ◽  
Proposed Model ◽  
Ablation Study ◽  
The Given ◽  
Video Question Answering

Most of the recent progresses on visual question answering are based on recurrent neural networks (RNNs) with attention. Despite the success, these models are often timeconsuming and having difficulties in modeling long range dependencies due to the sequential nature of RNNs. We propose a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering. Specifically, inspired by the success of self-attention in machine translation task, we propose a Positional Self-Attention to calculate the response at each position by attending to all positions within the same sequence, and then add representations of absolute positions. Therefore, PSAC can exploit the global dependencies of question and temporal information in the video, and make the process of question and video encoding executed in parallel. Furthermore, in addition to attending to the video features relevant to the given questions (i.e., video attention), we utilize the co-attention mechanism by simultaneously modeling “what words to listen to” (question attention). To the best of our knowledge, this is the first work of replacing RNNs with selfattention for the task of visual question answering. Experimental results of four tasks on the benchmark dataset show that our model significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task. Our model requires less computation time and achieves better performance compared with the RNNs-based methods. Additional ablation study demonstrates the effect of each component of our proposed model.

Sign in / Sign up

Export Citation Format

Share Document