Montserrat Ros

A Comparative Survey of VANET Clustering Techniques

A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles—most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming—the lack of realistic vehicular channel modeling—is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.

Recognizing human motions through mixture modeling of inertial data

Systems that recognize patterns in human motion are central to improvements in automation and human computer interaction. This work addresses challenges which arise in the context of recognizing arbitrary human actions from body-worn sensors. Chiefly the invariance to temporal scaling of events, coping with unlabeled data and estimating an appropriate model complexity. In order to deal with the severe case of unlabeled data, a method is proposed based on dynamic time alignment of Gaussian mixture model clusters for matching actions in an unsupervised temporal segmentation. In facilitation of this, an extensive corpus of continuous motion sequences composed of everyday tasks was recorded as analysis scenarios. The technique achieved an average accuracy of 72% for correctly merging actions performed by different participants. With labeled data and recognition models designed for particular classes, an accuracy of 89% was achieved in classifying the motion of participants left out of the modeling process. These results are contrasted with benchmark methods for recognition in a systematic validation revealing, in particular, an improved performance for mixture model prediction utilizing segments. HighlightsA method is proposed for unsupervised segment clustering of human motion capture data.Gaussian mixture models and dynamic time warping are used to compare similar data sequences.Human motion capture data was collected with a set of body-worn inertial sensors.The resultant classifier is compared with k-nearest-neighbor and support vector machine approaches.

Decoding Student Satisfaction: How to Manage and Improve the Laboratory Experience

The laboratory plays an important role in teaching engineering skills. An Electrical Engineering department at an Australian University implemented a reform to monitor and improve student satisfaction with the teaching laboratories. A Laboratory Manager was employed to oversee the quality of 27 courses containing instructional laboratories. Student satisfaction surveys were carried out on all relevant laboratories every year, and the data were used for continuous improvement. This paper will investigate the reforms that were implemented and outline a number of the improvements made. It also examines the programs overall impact on: (1) overall satisfaction; (2) laboratory notes; (3) learning experiences; (4) computer facilities; (5) engineering equipment; and (6) condition of the laboratory. Student satisfaction with the laboratories increased by 32% between 2007 and 2013. The results show that the laboratory notes (activity and clarity) and the quality of the equipment used are among the most influential factors on student satisfaction. In particular, it is important to have notes or resources that explain in some detail how to use and troubleshoot equipment and software used in the laboratory.

Wireless localisation network for patient tracking

We present an inexpensive and robust wireless localisation network that can track the location of patients in an indoor environment and monitor their physical status i.e. walking, running, etc. Static nodes are placed at predetermined positions in a building. The static nodes are used to determine the presence of the user in an area of a building. The user carries a mobile node on them. The localisation network was implemented using the small sized Fleck Nano wireless sensor. This platform also measured a userpsilas inertial movement using a three-axis accelerometer sensor. We also compared our localisation network to a commercially available indoor wireless localisation and tracking system. Further work involves developing a multi-hypothesis testing model tracking users, prediction human motion events and investigating the wireless network requirements of supporting large numbers of active users.

A Wireless Sensor Node Architecture Using Remote Power Charging, for Interaction Applications

The wireless sensor node architecture proposed in this paper is optimized for use in a wireless interactive point, listen and see system. In particular, we focus on developing a wireless sensor node that can be remotely charged by harvesting microwave energy. The current system implementation allows a user to access information from a remote sensor via their mobile computing device. These sensors are limited in complexity due to the limited power available, and are cumbersome since manual intervention is required to replace its batteries. We propose a system where battery powered wireless sensor nodes can be recharged by harvesting energy from a microwave Radio Frequency (RF) signal source. The remote power charging module of the wireless sensor node architecture consisted of an antenna array and a rectification circuit. A prototype of the antenna array and rectification circuit of the remote power charging module for the wireless sensor node was constructed and is presented in this paper.

Evaluation of realtime people tracking for indoor environments using ubiquitous motion sensors and limited wireless network infrastructure

Abstract We present the development and evaluation of a realtime indoor localisation system for tracking people. Our aim was to track a person’s indoor position using dead-reckoning, while limiting position error without depending on extensive wireless network infrastructure. The Indoor People Tracker used wearable motion sensors, a floor-plan map and a limited wireless sensor network for proximity ranging. We evaluated how the position accuracy of the Indoor People Tracker was affected by floor-plan map features, wireless proximity range and motion information. The advantage of the Indoor People Tracker was found; it was able to achieve accurate position resolution with minimal error, while not depending on wireless proximity.

Cervical Cancer Classification Using Gabor Filters

This paper presents a novel algorithm for computer-assisted classification of cervical cancers using digitized histology images of biopsies. Texture analysis of the nuclei structure is very important for classification of cervical cancer histology. In this paper we present a two-tier classification strategy using Gabor filter banks for local classification and abnormality spread for global taxonomy. The test data used in this work are digitized histology images of cervical biopsies acquired from the pathology laboratories in the Saiful An war Hospital in Indonesia. The images from over 500 subjects are categorized by the pathologists into five grades, benign, pre-cancer (CIN1, CIN2, CIN3) and malignant. In the algorithm developed in this work, a texture classification method using Gabor filter banks is implemented to segment the image into five possible regions: of background, normal, abnormal, basal and stroma cells. The global classification algorithm uses the segmented image for the final prognosis of the degree of malignancies from benign to malignant. The process of texture segmentation using the Gabor filter bank involves the application of filters for several spatial frequencies and orientations. The Gabor filter bank is applied to cervical histology images with six frequencies and four orientations. Feature vectors are formed, comprising the response of each pixel and its neighboring pixels to each filter. The feature vectors are then used to classify each pixel and its immediate neighbor pixels into five categories. Based on the spread of abnormalities on the epithelium layer, the cervical histology image is then classified. The classification results are then used to further classify the image into: 1) normal, 2) pre-cancer and 3) malignant. The pre-cancer is divided into: a) CIN 1, b) CIN 2 and c) CIN 3. The final system will take as input a biopsy image of the cervix containing the epithelium layer and provide the classification using our new approach, to assist the pathologist in cervical cancer diagnosis.

Seated Whole-Body Vibration Analysis, Technologies, and Modeling: A Survey

The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents a holistic literature survey of topics including the latest research in the area of vibration signal processing and modeling of the biodynamic response of the seated human to vibrations. This paper reviews recent sensing systems that are reported to measure the motion of the seated body. The data processing techniques that are currently accepted are surveyed and these include impedance, transmissibility measures, frequency response function estimation, and model development. A review of applications of biodynamic response analysis and modeling to seating vibration isolation technologies and vibration monitoring systems is presented within this paper. This survey paper provides a discussion on the direction that the future research in this field will aim toward based on the trends in the recent research and the introduction and application of new technologies.

Indoor localisation using a context-aware dynamic position tracking model

Indoor wireless localisation is a widely sought feature for use in logistics, health, and social networking applications. Low-powered localisation will become important for the next generation of pervasive media applications that operate on mobile platforms. We present an inexpensive and robust context-aware tracking system that can track the position of users in an indoor environment, using a wireless smart meter network. Our context-aware tracking system combines wireless trilateration with a dynamic position tracking model and a probability density map to estimate indoor positions. The localisation network consisted of power meter nodes placed at known positions in a building. The power meter nodes are tracked by mobile nodes which are carried by users to localise their position. We conducted an extensive trial of the context-aware tracking system and performed a comparison analysis with existing localisation techniques. The context-aware tracking system was able to localise a persons indoor position with an average error of 1.21 m.

Using online and multimedia resources to enhance the student learning experience in a telecommunications laboratory within an Australian University

ABSTRACT A laboratory component of an undergraduate telecommunications course consistently scored poorly for student learning experience on student surveys at an Australian university. Consultation with experienced academic staff revealed the need to modify the teaching resources available for the laboratory to include web-based multimedia and interactive resources. This new material was developed and made available to students and teaching staff in early 2011 via an Australian university e-learning package which was used to deliver the subject. The students and demonstrators were then encouraged to use this new resource to prepare for the three hour laboratory sessions. Surveys of students who took this laboratory in previous years were then compared to surveys of students using the latest version of the telecommunications laboratory in 2011 and 2012. The demonstrators themselves were also asked to provide feedback on their impressions of student learning. The comments from the laboratory demonstrators, feedback from the students, and assessment results indicate that the new online teaching material for both laboratory teaching staff and students has significantly improved the student learning experience. That this occurred two years in a row indicates that this improvement has ongoing benefits, irrespective of the teaching staff involved with the subject. The lessons learned can be applied to other similar learning environments.