Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation

Geomagnetic navigation leverages the ubiquitous Earth's magnetic signals to navigate missions, without dependence on GPS services or pre-stored geographic maps. It has drawn increasing attention and is promising particularly for long-range navigation into unexplored areas. Current geomagnetic navigation studies are still in the early stages with simulations and computational validations, without concrete efforts to develop cost-friendly test platforms that can empower deployment and experimental analysis of the developed approaches. This paper presents a hardware-in-the-loop simulation testbed to support geomagnetic navigation experimentation. Our testbed is dedicated to synthesizing geomagnetic field environment for the navigation. We develop the software in the testbed to simulate the dynamics of the navigation environment, and we build the hardware to generate the physical magnetic field, which follows and aligns with the simulated environment. The testbed aims to provide controllable magnetic field that can be used to experiment with geomagnetic navigation in labs, thus avoiding real and expensive navigation experiments, e.g., in the ocean, for validating navigation prototypes. We build the testbed with off-the-shelf hardware in an unshielded environment to reduce cost. We also develop the field generation control and hardware parameter optimization for quality magnetic field generation. We conduct a detailed performance analysis to show the quality of the field generation by the testbed, and we report the experimental results on performance indicators, including accuracy, uniformity, stability, and convergence of the generated field towards the target geomagnetic environment.

Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumption

This work presents an extended version of the Vehicle Energy Dataset (VED), which is a openly released large-scale dataset for vehicle energy consumption analysis. Compared with its original version, the extended VED (eVED) dataset is enhanced with accurate vehicle trip GPS coordinates, serving as a basis to associate the VED trip records with external information, e.g., road speed limit and intersections, from accessible map services to accumulate attributes that is essential in analyzing vehicle energy consumption. In particularly, we calibrate all the GPS trace records in the original VED data, upon which we associated the VED data with attributes extracted from the Geographic Information System (QGIS), the Overpass API, the Open Street Map API, and Google Maps API. The associated attributes include 12,609,170 records of road elevation, 12,203,044 of speed limit, 12,281,719 of speed limit with direction (in case the road is bi-directional), 584,551 of intersections, 429,638 of bus stop, 312,196 of crossings, 195,856 of traffic signals, 29,397 of stop signs, 5,848 of turning loops, 4,053 of railway crossings (level crossing), 3,554 of turning circles, and 2,938 of motorway junctions. With the accurate GPS coordinates and enriched features of the vehicle trip record, the obtained eVED dataset can provide a precise and abundant medium to feed a learning engine, especially a deep learning engine that is more demanding on data sufficiency and richness. Moreover, our software work for data calibration and enrichment can be reused to generate further vehicle trip datasets for specific user cases, contributing to deep insights into vehicle behaviors and traffic dynamics analyses. We anticipate that the eVED dataset and our data enrichment software can serve the academic and industrial automotive section as apparatus in developing future technologies.

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in database. Nevertheless, there lacks a clear and convincing contextualization of DP in image database, where individual images' indistinguishable contribution to a certain analysis can be achieved and observed when DP is exerted. As a result, the privacy-accuracy trade-off due to integrating DP is insufficiently demonstrated in the context of differentially-private image database. This work aims at contextualizing DP in image database by an explicit and intuitive demonstration of integrating conceptional differential privacy with images. To this end, we design a lightweight approach dedicating to privatizing image database as a whole and preserving the statistical semantics of the image database to an adjustable level, while making individual images' contribution to such statistics indistinguishable. The designed approach leverages principle component analysis (PCA) to reduce the raw image with large amount of attributes to a lower dimensional space whereby DP is performed, so as to decrease the DP load of calculating sensitivity attribute-by-attribute. The DP-exerted image data, which is not visible in its privatized format, is visualized through PCA inverse such that both a human and machine inspector can evaluate the privatization and quantify the privacy-accuracy trade-off in an analysis on the privatized image database. Using the devised approach, we demonstrate the contextualization of DP in images by two use cases based on deep learning models, where we show the indistinguishability of individual images induced by DP and the privatized images' retention of statistical semantics in deep learning tasks, which is elaborated by quantitative analyses on the privacy-accuracy trade-off under different privatization settings.