X-ORCA - A Biologically Inspired Low-Cost Localization System

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

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Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach

Current Alzheimer Research ◽

10.2174/1567205014666171120143800 ◽

2018 ◽

Vol 15 (2) ◽

pp. 139-148 ◽

Cited By ~ 12

Author(s):

Karmele Lopez-de-Ipina ◽

Unai Martinez-de-Lizarduy ◽

Pilar M. Calvo ◽

Jiri Mekyska ◽

Blanca Beitia ◽

...

Keyword(s):

Low Cost ◽

Speech Analysis ◽

Permutation Entropy ◽

Biologically Inspired ◽

Distant Future ◽

Non Invasive ◽

High Prevalence ◽

Non Linear ◽

Fast Pace ◽

Assessment Techniques

Objective: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Methods: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. Results: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Conclusion: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.

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A Low-Cost Collaborative Indoor Localization System Based on Smartphone Platform

2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) ◽

10.1109/cybermatics_2018.2018.00094 ◽

2018 ◽

Author(s):

Xiaoyuan Liang ◽

Guiling Wang ◽

Zhu Han

Keyword(s):

Indoor Localization ◽

Low Cost ◽

Localization System

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A Biologically Inspired Design Approach for Low Cost Solar Panel Systems

Volume 7: 2nd Biennial International Conference on Dynamics for Design; 26th International Conference on Design Theory and Methodology ◽

10.1115/detc2014-35275 ◽

2014 ◽

Cited By ~ 1

Author(s):

Olufunmilola Atilola ◽

Joseph Goodman ◽

Kathryn Nagel ◽

Julie Linsey

Keyword(s):

Design Thinking ◽

Low Cost ◽

Solar Panel ◽

Department Of Energy ◽

Successful Implementation ◽

Solar Pv ◽

Labor Costs ◽

Biologically Inspired ◽

Pv Systems ◽

Biologically Inspired Design

Biologically inspired design is the process of using biological systems as analogues to develop innovative solutions for engineering problems. This paper describes an effective and successful implementation of problem-driven biologically inspired design in a real-world problem. In support of the Department of Energy SunShot Initiative, a national collaborative effort to make solar energy cost-competitive with other forms of electricity by the end of the decade, solar panel designs were carried out by engineering and architectural design teams. Solar Photovoltaic (PV) systems were developed using analogical design, and more specifically, bio-inspired design. Some systems were also designed using non-biological analogues. Functional decompositions were employed as the first step in the design process, as a way to identify the key functions essential to the system’s reliability and cost effectiveness. Six key functions were identified. Analysis of the final designs by the teams showed that the solar panel system designs using biologically inspired analogues were more effective in meeting the six key functions identified during functional decomposition. Employing a combination of divergent and convergent design thinking is also discussed as a way for effective biologically inspired design. The top three designs selected for prototyping were biologically inspired and exceeded the project goal of reducing the installation and labor costs of solar PV systems by 50%.

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Hybrid method uses RSS and AoA to establish a low-cost localization system

2013 Fourth Argentine Symposium and Conference on Embedded Systems (SASE/CASE) ◽

10.1109/sase-case.2013.6636770 ◽

2013 ◽

Cited By ~ 3

Author(s):

Bruno de S. Muswieck ◽

Jumar L Russi ◽

Marcos V. T. Heckler

Keyword(s):

Hybrid Method ◽

Low Cost ◽

Localization System

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Mono-Camera-Based Robust Self-Localization Using LIDAR Intensity Map

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2020.p0624 ◽

2020 ◽

Vol 32 (3) ◽

pp. 624-633

Author(s):

Kei Sato ◽

Keisuke Yoneda ◽

Ryo Yanase ◽

Naoki Suganuma ◽

◽

...

Keyword(s):

Template Matching ◽

Low Cost ◽

The Self ◽

Light Detection And Ranging ◽

Automated Vehicles ◽

Light Detection ◽

Localization Method ◽

Localization System ◽

Intensity Map ◽

Map Data

An image-based self-localization method for automated vehicles is proposed herein. The general self-localization method estimates a vehicle’s location on a map by collating a predefined map with a sensor’s observation values. The same sensor, generally light detection and ranging (LIDAR), is used to acquire map data and observation values. In this study, to develop a low-cost self-localization system, we estimate the vehicle’s location on a LIDAR-created map using images captured by a mono-camera. The similarity distribution between a mono-camera image transformed into a bird’s-eye image and a map is created in advance by template matching the images. Furthermore, a method to estimate a vehicle’s location based on the acquired similarity is proposed. The proposed self-localization method is evaluated on the driving data from urban public roads; it is found that the proposed method improved the robustness of the self-localization system compared with the previous camera-based method.

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