Interoperability in IoT through the semantic profiling of objects

The emergence of smarter and broader people-oriented IoT applications and services requires interoperability at both data and knowledge levels. However, although some semantic IoT architectures have been proposed, achieving a high degree of interoperability requires dealing with a sea of non-integrated data, scattered across vertical silos. Also, these architectures do not fit into the machine-to-machine requirements, as data annotation has no knowledge on object interactions behind arriving data. This paper presents a vision of how to overcome these issues. More specifically, the semantic profiling of objects, through CoRE related standards, is envisaged as the key for data integration, allowing more powerful data annotation, validation, and reasoning. These are the key blocks for the development of intelligent applications.Portuguese Science and Technology Foundation (FCT) [UID/MULTI/00631/2013

A bounded heuristic for collection-based routing in wireless sensor networks

Wireless sensor networks are used to monitor and control physical phenomena and to provide interaction between clients and the physical environment. Clients have been typically users or user applications, but next generation wireless sensor networks will also work in machine-to-machine scenarios where some nodes can be interested in some other nodes' data. These scenarios may run the risk of becoming overloaded with messaging, a pernicious fact in particular for constrained networks where both bandwidth and power supply are limited. Resource collections can be used in wireless sensor networks to improve bandwidth usage and to reduce energy consumption, reducing the overall number of notification packets and wrapping overhead, required for the delivery of sensor data. This article proposes a heuristic algorithm for the planning of both routing and collections, in wireless sensor networks. Results show that collections are always worthwhile, and that the heuristic is able to find feasible and cost effective solutions, approaching its lower bound.FCT from Portugal within the CEOT research center [UID/MULTI/00631/2013

Colored nanoparticles for ecological dyeing of cellulosic fibers

Dyeing cellulosic fibres with reactive dyes wastes great quantities of salt and water. The objective of this work is to dye cellulosic fibers using colored nanoparticles (CNPs) as an alternative to dyeing with reactive dyes. CNPs do not need salt nor soaping after dyeing, consuming less than 50% of water

Key Phrase Extraction of Lightly Filtered Broadcast News

This paper explores the impact of light filtering on automatic key phrase extraction (AKE) applied to Broadcast News (BN). Key phrases are words and expressions that best characterize the content of a document. Key phrases are often used to index the document or as features in further processing. This makes improvements in AKE accuracy particularly important. We hypothesized that filtering out marginally relevant sentences from a document would improve AKE accuracy. Our experiments confirmed this hypothesis. Elimination of as little as 10% of the document sentences lead to a 2% improvement in AKE precision and recall. AKE is built over MAUI toolkit that follows a supervised learning approach. We trained and tested our AKE method on a gold standard made of 8 BN programs containing 110 manually annotated news stories. The experiments were conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio news/programs, running daily, and monitoring 12 TV and 4 radio channels.Comment: In 15th International Conference on Text, Speech and Dialogue (TSD 2012

Acquisition the profile of surfaces with complementary sensor fusion techniques

This paper presents complementary sensor fusion techniques for the acquisition of the profile of surfaces with minimum error using low cost sensors ultrasonic sensors. These surfaces are composed by areas with different depths, corners and specular surfaces. To minimize the constraints of sonar sensors, it was developed dedicated software and hardware, as well as an empirical model was obtained from real data. This model is based in two proposed concepts: Points of Constant Depth (PCD) and Areas of Constant Depth (ACD). Having this sonar model in mind, four sensor fusion techniques are used separately to validate the PCDs and decide the ACDs: average and variance, fuzzy controller and heuristic method based in rules. In this work a PUMA 560 manipulator was equipped with a CCD video camera on the shoulder and four ultrasonic sensors on the wrist, to acquire data to model the geometry of the part’s surface, exploiting the mobility of the robot. The CCD camera view defines the working area, while the ultrasonic sensors enable the acquisition of the surface profile. For the acquisition of the profile of surfaces with a minimum error different and complementary sensor fusion techniques are implemented and applied separately, namely the average and variance, kalman filter, fuzzy controller and heuristic method based in rules

A kalman filter for validate points and areas of constant depth in the acquisition of the profiles surfaces

This paper presents multisensor fusion techniques for the acquisition of the profile of surfaces with minimum error using low cost ultrasonic sensors. These surfaces are composed by areas with different depths, corners and specular surfaces. To minimize the constraints of sonar sensors, it was developed dedicated software and hardware, as well as an empirical model was obtained from real data. This model is based in two proposed concepts: Points of Constant Depth (PCD) and Areas of Constant Depth (ACD). Having this sonar model in mind, four sensor fusion techniques are used separately to validate the PCDs and decide the ACDs: average and variance, a simplified kalman filter and heuristic method based in rules. In this work a PUMA 560 manipulator was equipped with a CCD video camera and four ultrasonic sensors on the wrist, to acquire data for internally representation of the geometry of the part’s surface, exploiting the mobility of the robot. The CCD camera defines the working area while the ultrasonic sensors enable the acquisition of the surface profile