Pushing the efficiency of StereoNet: exploiting spatial sparsity

Current CNN-based stereo matching methods have demonstrated superior performance compared to traditional stereo matching methods. However, mapping these algorithms into embedded devices, which exhibit limited compute resources, and achieving high performance is a challenging task due to the high computational complexity of the CNN-based methods. The recently proposed StereoNet network, achieves disparity estimation with reduced complexity, whereas performance does not greatly deteriorate. Towards pushing this performance to complexity trade-off further, we propose an optimization applied to StereoNet that adapts the computations to the input data, steering the computations to the regions of the input that would benefit from the application of the CNN-based stereo matching algorithm, where the rest of the input is processed by a traditional, less computationally demanding method. Key to the proposed methodology is the introduction of a lightweight CNN that predicts the importance of r efining a region of the input to the quality of the final disparity map, allowing the system to trade-off computational complexity for disparity error on-demand, enabling the application of these methods to embedded systems with real-time requirements

ArchGenTool: a System-Independent Collaborative Tool for Robotic Architecture Design

Complex robotic architectures require a collaborative effort in design and adherence to the design in the implementation phse. ArchGentTool is a collaborative architecture generation tool which supports the design of the robotic architecture in a multi-level fashion. It comprises high-level conceptual analysis of the system to be designed, as well as low-level implementation breakdown of its functional components, acting complementary to the ROS framework. The tool facilitates reusability and expandability of the architecture to any robotic system, as it can be adapted to different specifications. A case study with the RAMCIP service robot is presente

An intelligent decision making and notification system based on a knowledge-enabled supervisory monitoring platform

This work describes a knowledge-based framework which is able to communicate online with process systems utilizing the existing infrastructure and provide knowledgeable actions to the operators. The proposed knowledge-enabled supervisory monitoring (KSM) platform combines components of existing standards such as ISA-95 in order to facilitate the data exchange between the automation system and the data repository. The functionalities of the developed platform are demonstrated using the requirements of an industrial control system of a continuous process and aim at the evaluation of the process and equipment performance based on predefined rules

Malfunction diagnosis in industrial process systems using data mining for knowledge discovery

The determination of abnormal behavior at process industries gains increasing interest as strict regulations and highly competitive operation conditions are regularly applied at the process systems. A synergetic approach in exploring the behavior of industrial processes is proposed, targeting at the discovery of patterns and implement fault detection (malfunction) diagnosis. The patterns are based on highly correlated time series. The concept is based on the fact that if independent time series are combined based on rules, we can extract scenarios of functional and non-functional situations so as to monitor hazardous procedures occurring in workplaces. The selected methods combine and apply actions on historically stored, experimental data from a chemical pilot plant, located at CERTH/CPERI. The implementation of the clustering and classification methods showed promising results of determining with great accuracy (97%) the potential abnormal situations

A Gamification Engine Architecture for Enhancing Behavioral Change Support Systems

This paper presents a gamified framework designed to offer behavioural change support and treatment adherence services to people living with Dementia (PLWD), their caregivers and medical/social professionals