JMM Abstracts 

Vol.9 No.1&2 November 30, 2013

A Wearable Sensor based Approach to Real-Time Fall Detection and Fine-Grained Activity Recognition (015-026)
Cuong Pham, Nguyen Ngoc Diep, and Tu Minh Phuong
We present a real-time fall detection and activity recognition system that is inexpensive and can be easily deployed using two Wii Remotes worn on human body. Continuously 3-dimentional data streams are segmented into sliding windows and then pre-processed for removing signal noises and filling missing samples. Features including Mean, Standard deviation, Energy, Entropy, Correlation between acceleration axes extracted from sliding windows are trained the activity models. The trained models are then used for detecting falls and recognizing 13 fine-grained activities including unknown activities in real-time. An experiment on 12 subjects was conducted to rigorously evaluate the system performance. With the recognition rates as high as 95% precision and recall for user dependent isolation training, 91% precision and recall for 10-fold cross validation and as high as 82% precision and recall for leave one subject out evaluations, the results demonstrated that the development of real-time, easy-to-deploy fall detection and activity recognition systems using low-cost sensors is feasible.

Experimental Results of a MANET Testbed for Different Settings of HELLO Packets of OLSR Protocol (027-038)
Masahiro Hiyama, Shinji Sakamoto, Elis Kulla, Makoto Ikeda, and Leonard Barolli
In Mobile Ad-hoc Networks (MANETs) the mobile terminals can be used in cooperation with each other, without having to depend on the network infrastructure. Recently, these terminals are low-cost, have high-performance and are mobile. Because the terminals are mobile, the routes change dynamically, so routing algorithms are important for operation of MANETs. In this paper, we investigate the behaviour of OLSR routing protocol for different values of HELLO sending interval and validity time. We conduct real experiments in a MANET tested. We design and implement two experimental scenarios in our academic environment and investigate their performance behaviour for different number of hops.

Recognizing Landscapes: Can We Change the Point of View of Geographic Data? (039-052)
Luigi Barazzetti, Raffaella Brumana, Daniela Oreni, and Fabio Roncoroni
This paper presents a methodology able to handle georeferenced panoramas (GeoPans) projected on 3D models for the integration of landscapes into digital environments. This is not a simple task because the typical visualization (say vertical point of view) through geographic data and GIS software does not fulfil a fundamental request: the virtual reproduction of the human eye at head height. This means that a transition from aerial images to ground (terrestrial) data is mandatory. In addition, an improvement of SDI able to generate innovative typologies of representation is needed. In this work a methodological approach aimed at rediscovering and correlating 3D reconstructions of landscapes with the typical human vision is illustrated. This contribution investigates the potential of panoramic view reconstruction and simulation from images acquired by RC/UAV and by multi-sensor terrestrial platforms (photoGPS) along with existing cartographic data. The main aim is the generation of multiple visual models able to simulate real scenarios at head height (or low altitude above ground). Examples and case studies are illustrated and discussed to prove the complexity of the problem, which requires not only new algorithms and procedures for data acquisition and processing, but also a modification of the traditional 2.5D representation of geographic data.

Energy-Aware Passive Replication of Processes (053-065)
Dilawaer Duolikun, Ailixier Aikebaier, Tomoya Enokido, and Makoto Takizawa
In information systems, processes requested by clients have to be performed on servers so that not only QoS (quality of service) requirements like response time are satisfied but also the total electric power consumed by servers to perform processes has to be reduced. Furthermore, each process has to be reliably performed in the presence of server faults. In our approach to reliably performing processes, each process is redundantly performed on multiple servers. The more number of servers a process is performed on, the more reliably the process can be performed but the more amount of electric power is consumed by the servers. Hence, it is critical to discuss how to reliably and energy-efficiently perform processes on multiple servers. In this paper, we discuss how to reduce the total electric power consumed by servers in a cluster where each request process is passively replicated on multiple servers. Here, a process is performed on only one primary server while taking checkpoints and sending the checkpoints to secondary servers. If the primary server is faulty, one of the secondary servers takes over the faulty primary server and the process is performed from the check point on the new primary server. We evaluate the energy-aware passive replication scheme of a process in terms of total power consumption and average execution time and response time of each process in presence of server fault.

Thermographic Analysis from UAV Platforms for Energy Efficiency Retrofit Applications (066-082)
Mattia Previtali, Luigi Barazzetti, Raffaella Brumana, and Fabio Roncoroni

Thermal efficiency of building is a fundamental aspect in different countries to reach energy consumption reduction. However, even if a great attention is paid to build new zero-energy buildings, low attention is paid to retrofit existing ones. A fast analysis of existing buildings with Infrared Thermography (IRT) has proved to be an adequate and efficient technique. Indeed, IRT can be used to determine energy efficiency and to detect defects like thermal bridges and heat losses. However, both surface temperature and geometry are needed for a reliable evaluation of thermal efficiency, where spatial relationships are important to localize thermal defects and quantify the affected surfaces. For this reason, integration between Building Information Models (BIMs) and Infrared Thermography (IRT) can be a powerful tool to combine geometric information with thermal data in the same model. In this paper a methodology for automated generation of 3D model of buildings from laser data and integration with thermal images is presented. The developed methodology allows also fusion of thermal data acquired from different cameras and platforms. In particular, this paper will focus on thermal images acquired by an Unmanned Aerial Vehicle (UAV). The proposed methodology is suitable for fast building inspections aimed at detecting the thermal anomalies in a construction. Its applicability was tested on different buildings demonstrating the performance of the procedure and its valid support in thermal surveys.

Interactive Mesh Deformation in Multiresolution through Augmented Reality (083-100)
   Renan A. Dembogurski , Rodrigo Luis De Souza Da Silva, Marcelo Bernardes Vieira, and Bruno J. Dembogurski
This work presents a method that allows the deformation of a terrain by modifying its heightmap in an augmented reality environment. The hierarchical structure of A4-8 meshes was used to represent terrains. This structure defines a parameter space to calculate the coordinates of a field in the $\mathbb{R}^3$ Euclidean space. In particular, this paper deals with the problem of modeling spherical terrains. An error metric dependent on the observer and the geometry of the land used for its observation and modeling. The results demonstrate that the use of A4-8 mesh combined with the tangible augmented reality system is flexible to shape spherical terrains and can be easily modified to deal with other topologies, such as the torus and the cylinder. The development of an efficient and intuitive to use method for mesh generation, based on augmented reality markers, is the main contribution of this work.

A Comparison Study of Simulated Annealing and Genetic Algorithm for Node Placement Problem in Wireless Mesh Networks (101-110)
Shinji Sakamoto, Elis Kulla, Tetsuya Oda, Makoto Ikeda, Leonard Barolli, and Fatos Xhafa
One of the key advantages of Wireless Mesh Networks~(WMNs) is their importance for providing cost-efficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, we compare Simulated Annealing~(SA) and Genetic Algorithm~(GA) by simulations for node placement problem. We want to find the optimal distribution of router nodes in order to provide the best network connectivity and user coverage in a set of randomly distributed clients. From the simulation results, both algorithms converge to the maximum size of GC. However, according to the number of covered mesh clients SA converges faster.

Using the Dual-Level Modeling Approach to Developing Applications in the Pervasive Healthcare Environment (111-127)
Joao L. Cardoso de Moraes, Wanderley Lopes de Souza, Luis Ferreira Pires, Luciana Tricai Cavalini, and Antonio Francisco do Prado
Health information technology is the area of IT involving the design, development, creation, use and maintenance of information systems for the healthcare industry. Automated and interoperable healthcare information systems are expected to lower costs, improve efficiency and reduce error, while also providing better consumer care and service. Pervasive Healthcare focuses on the use of new technologies, tools, and services, to help patients to play a more active role in the treatment of their conditions. Pervasive Healthcare environments demand a huge amount of information exchange, and specific technologies has been proposed to provide interoperability between the systems that comprise such environments. However, the complexity of these technologies makes it difficult to fully adopt them and to migrate Centered Healthcare Environments to Pervasive Healthcare Environments. Therefore, this paper proposes an approach to develop applications in the Pervasive Healthcare environment, through the use of dual-level modeling based on Archetypes. This approach was demonstrated and evaluated in a controlled experiment that we conducted in the cardiology department of a hospital located in the city of Marília (São Paulo, Brazil). An application was developed to evaluate this approach, and the results showed that the approach is suitable for facilitating the development of healthcare systems by offering generic and powerful approach capabilities.

A Hidden Markov Model for Detection & Classification of Arm Action in Cricket Using Wearable Sensors (128-144)
Saad Qaisar, Sahar Imtiaz, Fatma Faruq, Amna Jamal, Wafa Iqbal, Paul Glazier, and Sungyoung Lee
Hidden Markov Models (HMM) have been used for to accurately model, detect and classify key phenomenon. In this manuscript, we propose use of HMM for detection and classification of arm action in the game of cricket. The technique uses sensor data gathered from wearable sensors placed at wrist, elbow and shoulder. The sensor data consists of both displacement and rotational information collected through a combination of accelerometer and gyroscope placed at each joint. A Bluetooth transceiver is attached to the arm in order to wirelessly transfer the gathered data to the base station. A K-means clustering algorithm classifies the current position and angular rotation of the joint for each of the sensor placements. A Markov chain then determines the chain of sequence for a set of joint movements (displacement and angular rotation) to classify it as a specific arm motion. A Hidden Markov Model determines the previous state of arm motion in order to classify the current state and hence, the current action since the movements happen in progression, when following the other. Experiments show an accuracy of up to 100% in correctly determining the arm action against a model built around a trace-set collected from a sports biomechanics expert. The proposed model has applications in cricket coaching and technique adaptation both for novice and trained players.

A Mobile Application for Robust Feature Extraction and Cultivar Classification of Leaves (145-154)
Dominik L. Michels and Gerrit A. Sobottka
We illustrate the development of an application for cultivar classification of leaf images based on the extraction of the network of its main veins that runs on mobile devices like smart phones or tablets. Such mobile devices can be docked to farming robots in order to support the farming process. Our application uses an efficient Gabor filter-based tracing algorithm which is able to perform a robust network extraction. The results are used as input data for the classification with a support vector machine. In order to demonstrate the advantageous behavior and the robustness of this method, we perform an evaluation on a test set consisting of 150 light transmitted images of different vine leaves.

A Visualization System for Mobile Ad-hoc Networks (155-170)
Akio Koyam, Shohei Sato, Leonard Barolli, and Makoto Takiz
Mobile ad-hoc networks (MANETs) are autonomous distributed networks, where mobile nodes communicate with each other using wireless links. Since the communication is done by wireless links, it is difficult to grasp the network situation. It becomes even more difficult when the number of nodes is increased. In this paper, we propose a visualization system for MANETs. The system can mainly visualize the network topology, state of nodes, and packet flows in MANETs using mobile PCs and wireless LAN cards. The multi-hop communication function needed for visualization of MANETs was also implemented on the application layer. For visualization of network topology, we implemented three modes: GPS mode, Hop Tree mode and manual mode. Furthermore, in order to show the effectiveness of the system, we implemented DSR protocol and visualized its packet flow. We verified by experiments that the visualization system can promptly represent the network topology, the state of each node, and the packet flow.

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