Expertise/ extensive practical experience in the following areas: Computer vision, digital signal processing (DSP), object tracking OR object detection, multi-sensor data fusion, (Particle filters/ Kalman filters...)

Recent advances in computational methods, material science, and manufacturing technologies reveal promising potentials for using geometrically complex parts to optimize the performance of structural systems. However, this potential has not yet been activated partly due to the immaturity of nondestructive testing (NDT) of such complex parts. Process compensated resonance testing (PCRT) is one of the methods that are in the focus of researchers for this purpose. The key to success for the PCRT approach is to use high-frequency vibration data in conjunction with statistical pattern recognition methods for supervised classification of parts in terms of their structural quality. In this paper, a multi classifier selection-fusion framework based on the Dempster-Shafer theory is proposed. Two new weighting approaches are introduced to enhance the fusion performance, and as such the classification performance. The effectiveness of the proposed framework is validated by its application to six UCI machine learning datasets and one experimental dataset collected from polycrystalline Nickel alloy first-stage turbine blades with a variety of damage features. Comparison with four state-of-the-art fusion techniques shows the good performance of the introduced classifier selection-fusion framework.

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, artificial intelligence (AI) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using AI technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. Based on the over 30 representative articles selected from the nearly 300 related publications, this article summarizes the state of the art, advantages, and challenges on each aspect. Finally, it summarizes nine advantages and nine challenges of AI-enhanced inertial sensing and then points out future research directions.

We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach. The components of the dataset posteriors are assigned to the proposed global model components by solving a regularized variant of the assignment problem. The global components are then updated based on these assignments by their mean under a KL divergence. For exponential family variational distributions, our formulation leads to an efficient non-parametric algorithm for computing the fused model. Our algorithm is easy to describe and implement, efficient, and competitive with state-of-the-art on motion capture analysis, topic modeling, and federated learning of Bayesian neural networks.

Talend is an open source data integration platform. It provides various software and services for data integration, data management, enterprise application integration, data quality, cloud storage and Big Data. Talend first came into the market in 2005 as the first commercial open source software vendor of data integration software. In October 2006, Talend launched it's very first product – Talend Open Studio which is currently known as Talend Open Studio for Data Integration. Since then, it has released a wide range of products which are used quite favorably in the market.

The automatic and accurate interlinking of geospatial data poses an important scientific challenge, with direct application in several business fields. The major requirement is achieving high accuracy in identifying similar entities within datasets. For example, in a cadastral database, it is crucial that the land parcels, that were gathered from several different databases, are uniquely and clearly identified. In another example, for a geo-marketing company, it is of high importance to be able to accurately cross-reference the location/addresses of customers and companies, so that they are properly targeted. LinkGeoML aims at researching, developing and extending machine learning methods, utilizing the vast amount of available, open geospatial data, in order to implement automated and highly accurate algorithms for interlinking geospatial entities. The proposed methods will implement novel training features, based on domain knowledge and on the analysis of open and proprietary geospatial datasets.

Ackroo Inc., a loyalty marketing, gift card and payments technology and services provider, is pleased to announce that they have launched Ackroo BI, Ackroo's business intelligence data services product. As a data driven MarTech company, Ackroo now offers an end to end data solution that combines an Ackroo developed DataWarehouse for ingestion of ALL sales and transaction data, a storage and data transformation tool in order to process, store and sort the ingested information, plus an integrated data presentation and visualization tool for custom dashboards and reports. Clients can also choose to use their own visualization tool and just utilize Ackroo's Enterprise DataWarehouse and leverage the Ackroo data engineering services team to support their data needs. The solution will provide Ackroo merchants a centralized and unified data set to better understand not just their loyalty and gift card data but ALL purchase data in order to make better marketing and business decisions and to truly understand ROI. For Ackroo this means even further differentiation in the marketplace and an additional revenue stream that the Company expects will have a significant impact on their organic growth in the years ahead.

Python Vs R key differences in commands and syntaxes 5.0 (1 rating) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. You will learn and reconcile the key differences in commands of R programming and Python. We have realized that professionals and students have to learn multiple languages to keep up to the needs of clients and organizations. R and Python are most common languages for a Data scientist/ Business Intelligence and big data developers and it often causes confusion between 2 languages. Steven is a IT/ETL data developer and data scientist and has extensive industry experience into large variety of technologies.

Amazon SageMaker Ground Truth now supports labeling 3D point cloud data. For more information about the launched feature set, see this AWS News Blog post. In this blog post, we specifically cover how to perform the required data transformations of your 3D point cloud data to create a labeling job in SageMaker Ground Truth for 3D object tracking use cases. Autonomous vehicle (AV) companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment.

Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. We build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors' data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.