New weighting method to synthesize a zero sidelobe correlation filter for burst signals

2007 ◽  
Vol 90 (10) ◽  
pp. 25-35 ◽  
Author(s):  
Mitsuyoshi Shinonaga ◽  
Junichiro Suzuki ◽  
Shinichi Yajima ◽  
Shinkichi Nishimoto
Keyword(s):  
Correlation Filter ◽  
Weighting Method

Multi-template Correlation Filter Tracking Based on Deep Feature

2019 ◽  
Vol 31 (5) ◽  
pp. 792
Author(s):  
Zongmin Li ◽  
Hongjiao Fu ◽  
Yujie Liu ◽  
Hua Li
Keyword(s):  
Correlation Filter ◽  
Deep Feature

scholarly journals A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1030
Author(s):  
Oscar V. De la Torre-Torres ◽  
Evaristo Galeana-Figueroa ◽  
José Álvarez-García
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Portfolio Performance ◽  
Trading Costs ◽  
Weighting Method ◽  
Treasury Bills ◽  
Markov Switching Models ◽  
Switching Models ◽  
Asset Portfolio ◽  
Stock Portfolio

In the present paper, we test the benefit of using Markov-Switching models and volatility futures diversification in a Euro-based stock portfolio. With weekly data of the Eurostoxx 50 (ESTOXX50) stock index, we forecasted the smoothed regime-specific probabilities at T + 1 and used them as the weighting method of a diversified portfolio in ESTOXX50 and ESTOSS50 volatility index (VSTOXX) futures. With the estimated smoothed probabilities from 9 July 2009 to 29 September 2020, we simulated the performance of three theoretical investors who paid different trading costs and invested in ESTOXX50 during calm periods (low volatility regime) or VSTOXX futures and the three-month German treasury bills in distressed or highly distressed periods (high and extreme volatility regimes). Our results suggest that diversification benefits hold in the short-term, but if a given investor manages a two-asset portfolio with ESTOXX50 and our simulated portfolios, the stock portfolio’s performance is enhanced significantly, in the long term, with the presence of trading costs. These results are of use to practitioners for algorithmic and active trading applications in ESTOXX50 ETFs and VSTOXX futures.

scholarly journals Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 437
Author(s):  
Yuya Onozuka ◽  
Ryosuke Matsumi ◽  
Motoki Shino
Keyword(s):  
Visual Information ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Weighting Method ◽  
Personal Mobility ◽  
Human Understanding ◽  
Autonomous Mobility ◽  
Weakly Supervised ◽  
Traffic Rules

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.

Learning spatio-temporal correlation filter for visual tracking

2021 ◽  
Vol 436 ◽  
pp. 273-282
Author(s):  
Youmin Yan ◽  
Xixian Guo ◽  
Jin Tang ◽  
Chenglong Li ◽  
Xin Wang
Keyword(s):  
Visual Tracking ◽  
Temporal Correlation ◽  
Correlation Filter ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document