scholarly journals A parallel implementation of a multisensor feature-based range-estimation method

2002 ◽  
Author(s):  
R.E. Suorsa ◽  
B. Sridhar
Keyword(s):  
Parallel Implementation ◽  
Estimation Method ◽  
Range Estimation ◽  
Feature Based

Cost Modeling and Range Estimation for Top-k Retrieval in Relational Databases

2011 ◽  
pp. 295-315
Author(s):  
Anteneh Ayanso ◽  
Paulo B. Goes ◽  
Kumar Mehta
Keyword(s):  
Relational Databases ◽  
Estimation Method ◽  
Synthetic Data ◽  
Cost Modeling ◽  
Data Sets ◽  
Range Estimation ◽  
Mapping Techniques ◽  
The Cost ◽  
Query Estimation ◽  
Query Mapping

Relational databases have increasingly become the basis for a wide range of applications that require efficient methods for exploratory search and retrieval. Top-k retrieval addresses this need and involves finding a limited number of records whose attribute values are the closest to those specified in a query. One of the approaches in the recent literature is query-mapping which deals with converting top-k queries into equivalent range queries that relational database management systems (RDBMSs) normally support. This approach combines the advantages of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures and indexing techniques to handle top-k queries separately. This paper reviews existing query-mapping techniques in the literature and presents a range query estimation method based on cost modeling. Experiments on real world and synthetic data sets show that the cost-based range estimation method performs at least as well as prior methods and avoids the need to calibrate workloads on specific database contents.

A Study on the Robustness of Pitch-Range Estimation from Brief Speech Segments

2020 ◽  
Vol 30 (01) ◽  
pp. 2050003
Author(s):  
Wenjie Peng ◽  
Kaiqi Fu ◽  
Wei Zhang ◽  
Yanlu Xie ◽  
Jinsong Zhang
Keyword(s):  
Speaker Recognition ◽  
Native Speakers ◽  
Estimation Method ◽  
Experimental Results ◽  
Percentage Error ◽  
Range Estimation ◽  
Pitch Range ◽  
Speech Segment ◽  
The Mean ◽  
Speech Segments

Pitch-range estimation from brief speech segments could bring benefits to many tasks like automatic speech recognition and speaker recognition. To estimate pitch range, previous studies have proposed to utilize deep-learning-based models with spectrum information as input. They demonstrated that such method works and could still achieve reliable estimation results when the speech segment is as brief as 300 ms. In this study, we evaluated the robustness of this method. We take the following scenarios into account: (1) a large number of training speakers; (2) different language backgrounds; and (3) monosyllabic utterances with different tones. Experimental results showed that: (1) The use of a large number of training speakers improved the estimation accuracies. (2) The mean absolute percentage error (MAPE) rate evaluated on the L2 speakers is similar to that on the native speakers. (3) Different tonal information will affect the LSTM-based model, but this influence is limited compared to the baseline method which calculates pitch-range targets from the distribution of [Formula: see text]0 values. These experimental results verified the efficiency of the LSTM-based pitch-range estimation method.

scholarly journals Correction: Lineage Range Estimation Method Reveals Fine-Scale Endemism Linked to Pleistocene Stability in Australian Rainforest Herpetofauna

PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0169726 ◽  
Author(s):  
Dan F. Rosauer ◽  
Renee A. Catullo ◽  
Jeremy VanDerWal ◽  
Adnan Moussalli ◽  
Conrad J. Hoskin ◽  
...  
Keyword(s):  
Estimation Method ◽  
Fine Scale ◽  
Range Estimation

Accurate Range Estimation Using Terrain Preview for Power Wheelchairs

2012 ◽  
Author(s):  
Xiaoli Zhang ◽  
Wei Zhang
Keyword(s):  
Estimation Method ◽  
Operating Conditions ◽  
Critical Parameters ◽  
Range Estimation ◽  
Positioning Systems ◽  
Power Wheelchairs ◽  
Global Positioning ◽  
History Of ◽  
Li Ion ◽  
Estimation System

Battery-powered wheelchairs require accurate and reliable range prediction to offer the maximum visibility of the current battery state to users, and to help them schedule future trip plans as well as fulfill the maximum battery economy potential. It is also one of the most critical parameters of wheelchairs to ensure the safety of users. However, range prediction is a very complicated issue by the fact that batteries are subject to current profiles, external influences, history of battery use, and aging. The prediction is even more challenging with unknown future driving conditions. The aim of this paper is to use a preview of a 3-D map, geographic information systems, and global positioning systems to develop an accurate range estimation system for battery-powered wheelchairs. This allows range prediction based on previewed driving road conditions. The nonlinearity of Li-ion batteries is also taken into consideration by using a circuit based battery model. Altogether, this methodology offers robustness and accuracy under varying operating conditions. Simulation results are presented to validate the proposed estimation method.

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