Martina Eckert

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

Context Aware Middleware Architectures: Survey and Challenges

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs)

This article proposes a novel method for detecting forest fires, through the use of a new color index, called the Forest Fire Detection Index (FFDI), developed by the authors. The index is based on methods for vegetation classification and has been adapted to detect the tonalities of flames and smoke; the latter could be included adaptively into the Regions of Interest (RoIs) with the help of a variable factor. Multiple tests have been performed upon database imagery and present promising results: a detection precision of 96.82% has been achieved for image sizes of 960 × 540 pixels at a processing time of 0.0447 seconds. This achievement would lead to a performance of 22 f/s, for smaller images, while up to 54 f/s could be reached by maintaining a similar detection precision. Additional tests have been performed on fires in their early stages, achieving a precision rate of p = 96.62%. The method could be used in real-time in Unmanned Aerial Systems (UASs), with the aim of monitoring a wider area than through fixed surveillance systems. Thus, it would result in more cost-effective outcomes than conventional systems implemented in helicopters or satellites. UASs could also reach inaccessible locations without jeopardizing people’s safety. On-going work includes implementation into a commercially available drone.

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

Intensity normalization of sidescan sonar imagery

Sonar imaging is currently the exemplary choice used in underwater imaging. However, since sound signals are absorbed by water, an image acquired by a sonar will have gradient illumination; thus, underwater maps will be difficult to process. In this work, we investigated this phenomenon with the objective to propose methods to normalize the images with regard to illumination. We propose to use MIxed exponential Regression Analysis (MIRA) estimated from each image that requires normalization. Two sidescan sonars have been used to capture the seabed in Lake Vattern in Sweden in two opposite directions west-east and east-west; hence, the task is extremely difficult due to differences in the acoustic shadows. Using the structural similarity index, we performed similarity analyses between corresponding regions extracted from the sonar images. Results showed that MIRA has superior normalization performance. This work has been carried out as part of the SWARMs project (http://www.swarms.eu/).

A modular middleware approach for exergaming

This paper presents the design of a new exergaming environment consisting of a modular middleware tool aimed at serving for intelligent adventure games. The middleware provides a modular and user-adaptive interface for data exchange between different devices (to date it supports a motion capture camera, a mobile phone, and a VR headset) and Blender. The target group is formed by young people between ages 6 to 26 with different physical diseases (muscular dystrophy, cerebral palsy, accidents, etc.). The gaming environment focuses especially on user awareness, immersion, and adaptability to special needs.

Usage of VR Headsets for Rehabilitation Exergames

The work presented here is part of a large project aimed at finding new ways to tackle exergames used for physical rehabilitation. The preferred user group consists of physically impaired who normally cannot use commercially available games; our approach wants to fill a niche and allow them to get the same playing experience like healthy. Four exercises were implemented with the Blender Game engine and connected to a motion capture device (Kinect) via a modular middleware. The games incorporate special features that enhance weak user movements, such that the avatar reacts in the same way as for persons without physical restrictions. Additionally, virtual reality glasses have been integrated to achieve a more immersive feeling during play. In this work, we compare the results of preliminary user tests, performed with and without VR glasses. Test outcomes are good for motion amplification in some of the games but do not present generally better results when using the VR glasses.

AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.

MoKey: A versatile exergame creator for everyday usage

ABSTRACT Currently, virtual applications for physical exercises are highly appreciated as rehabilitation instruments. This article presents a middleware called “MoKey” (Motion Keyboard), which converts standard off-the-shelf software into exergames (exercise games). A configurable set of gestures, captured by a motion capture camera, is translated into the key strokes required by the chosen software. The present study assesses the tool regarding usability and viability on a heterogeneous group of 11 participants, aged 5 to 51, with moderate to severe disabilities, and mostly bound to a wheelchair. In comparison with FAAST (The Flexible Action and Articulated Skeleton Toolkit), MoKey achieved better results in terms of ease of use and computational load. The viability as an exergame creator tool was proven with help of four applications (PowerPoint®, e-book reader, Skype®, and Tetris). Success rates of up to 91% have been achieved, subjective perception was rated with 4.5 points (from 0–5). The middleware provides increased motivation due to the use of favorite software and the advantage of exploiting it for exercise. Used together with communication software or online games, social inclusion can be stimulated. The therapists can employ the tool to monitor the correctness and progress of the exercises.

Fast facial expression recognition for emotion awareness disposal

This paper presents a simple and fast expression recognition algorithm aimed at running in a secondary plane to provide emotion awareness for primary applications as e.g. exergames, in real time. The algorithm is based on the extraction of 19 facial landmarks which are used to detect some of the Action Units (AUs) defined in the Facial Action Coding System (FACS) and a newly created one. In addition, the new concept of Combined Action Units (CAUs) is presented. Those are grouped AUs which are detected as a unit. The applied emotion classification is based on logical rules, no learning is involved. First implementations have been made on a mobile platform.