Marco Calabrese

Experimental System to Support Real-Time Driving Pattern Recognition

This work proposes an advanced driving information system that, using the acceleration signature provided by low cost sensors and a GPS receiver, infers information on the driving behaviour. The proposed system uses pattern matching to identify and classify driving styles. Sensor data are quantified in terms of fuzzy concepts on the driving style. The GPS positioning datum is used to recognize trajectory (rectilinear, curving) while the acceleration signature is bounded within the detected trajectory. Rules of inference are applied to the combination of the sensor outputs. The system is real-time and it is based on a low-cost embedded lightweight architecture which has been presented in a previous work.

Hierarchical-granularity holonic modelling

Design criteria for distributed and pervasive intelligent systems, such as Multi Agent Systems (MAS), are generally led by the functional decomposition of the given application-dependent knowledge. Consequently, changes either in the problem semantics or in the granularity level description may have a significant impact on the overall system re-engineering process. In order to tackle better these issues, a novel framework called Hierarchical-Granularity Holonic Model (HGHM) is introduced as a holon-based approach to distributed intelligent systems modelling. A holon is an agent endowed with special features. Seen from the outside, a holon behaves like an intelligent agent; seen from the inside, it appears to be decomposable into other holons. This property allows for modelling complex distributed systems at multiple hierarchical-granularity levels by exploiting the different abstraction layers at which the design process is carried out. The major benefit of the proposed approach against traditional holonic systems and MAS is that the entire HGHM-based architecture can be derived directly from the problem ontology as a hierarchical composition of self-similar, modular blocks. This helps designers focussing more on knowledge representation at different granularity levels which is a very basic process, as in top–down problem decomposition. Starting from the literature on holonic systems, a theoretical model of HGHM is introduced and an architectural model is derived accordingly. Finally, a customized application for the case study of distributed indoor air quality monitoring systems is commented and improvements in terms of system design with respect to well-established solutions are considered.

Computational-based volatile organic compounds discrimination: An experimental low-cost setup

In this work, an array of low-cost cross-sensitive sensors is used for discriminating the best candidate within a set of volatile organic compounds (VOCs). The challenge of our experimental setting is to deal with the problems of low selectivity, especially in normal operating conditions, so that ambiguous sensor responses (i.e. referable to more than one VOC) can be given, at least, a qualitative interpretation. In order to carry out the signal disambiguation task, a computational technique employing simple classifying rules and fuzzy descriptions has been engineered. The basic idea is that, if the same gas is actually measured by two or more sensors, then the estimated concentrations will show a low variance, with an accuracy related to the number of concordant sensors. Experiments show that, despite the cheapness of the setup and the coarse-grained nature of the provided response, encouraging results can be obtained and prospective work can follow.

GPS-aided lightweight architecture to support multi-sensor data synchronization

GPS sentences carry the UTC time information. In the case of extended sensor grids or world wide sensor networks the UTC information appears to be very attractive but one risk is to have degraded timing accuracy imputable to much noise on the signal transmission path. Well known problems are time delays due to ionosphere, troposphere or receiver hardware specifics. The software phase instead is often skipped although it can represent actually a noisy element for the correct synchronization. This paper presents a GPS-based lightweight low-cost system architecture that can support multi-sensor data synchronization through an accurate timestamp of the incoming data streams. The proposed architecture handles both hardware and software problems in order to achieve correct post-processing data synchronization by means of software timestamping. Particular attention is paid to the combined hardware/software solution that minimises the overall delay time before the software timestamp event. The proposed architecture is also suitable to provide accurate evaluation of software algorithms impact over multi-sensor measurements.

Discriminating gaseous emission patterns in low-cost sensor setups

This work presents a two-step heuristic that employs extremely low-cost sensors for gaseous emission event discrimination. These events are triggered by particular patterns of sensor responses possibly occurring when a certain gas is emitted; patterns are then used to produce human-understandable inference rules describing the kind of emission measured. The technique, challenged by the high cross-sensitivity of the employed sensors, is based on two steps: first, sensor response patterns are extracted (unsupervisedly) from measurement signals by means of a recently proposed computational intelligence technique; second, a ‘credibility index’ is applied (supervisedly) to each pattern via fuzzy membership functions. The outcome is a set of IF THEN statements weighted by fuzzy constraints. Experiments show that such inferences allow for accurate gaseous emission event discrimination.

Dialogue-oriented interface for linguistic human-computer interaction: A chat-based application

This work presents a prototype conversational system enabling human-computer interaction by using natural language expression. As an enhancement to well-known conversational agents like chatbots, in the proposed setting, human-machine dialogue is intended as a query/answer monotonic process aimed at minimizing semantic ambiguity within communication and delivering the required service. When user queries are ambiguous, hence semantically distant from the set of possible recognized interpretations, the system instantiates a dialogue with the user. In this case, the system provides suggestions on how to reformulate the query until a valid form is reached; this feed-back makes the dialogue-oriented interaction process resemble an ordinary chat (in the very restricted domain of system services) but with a machine interlocutor. The popularity of the chat as a synchronous communication instrument lets our proposal be suitable for a great variety of applications.

Taxonomies and Ontologies in Web Semantic Applications: The New Emerging Semantic Lexicon-Based Model

This paper addresses the new emerging approach of Semantic Lexicon-based systems for modeling semantic Web applications. The paper endeavors to shed lights on some misconceptions in the literature about the use of taxonomy and ontology as interchangeable terms, defining a midpoint between the two extremes. Semantic Lexicon is considered as the right match between the semantic layer represented by a given ontology and the lexical layer represented by a given taxonomy. Agent-based implementations that employ WordNet as Semantic Lexicon are currently being tested within this framework.

Self-descriptive IF THEN rules from signal measurements: A holonic-based computational technique

A holon is a bio-inspired conceptual entity that, like cells in a living organism, behaves as a part and a whole at the same time. Holonic systems have been the subject of intense research in the latest years due to their properties such as self-organization, self-similarity and capability of handling hierarchically-nested granularity levels. Lesser attention indeed has been paid by engineers to the aspect of self-description, i. e. the ability to describe itself in terms of self-contained descriptors. Self-description can be useful in measurement settings where the only available knowledge is embedded in data in terms of hidden rules behind observed signals. In this work, a heuristic technique is employed to extract self-descriptive IF THEN rules from measurement signals. These rules are considered holonic in that they represent a whole described in terms of relationships among their parts. An example taken from a real measurement scenario is reported and commented in detail.

An ontology-based approach to human telepresence

Detecting human presence automatically is a challenging task since several environmental parameters may affect the quality and the continuity of detection. Although many techniques have been developed so far in the literature to solve this problem, they generally rely on well-defined operational context. Hence, they are sensitive to uncontrolled variables and unpredicted events. In this work an ontology-based approach to human telepresence detection is presented. Contrarily to classic sensor-driven techniques, a top-down methodology is applied. Starting from a formal description of the problem ontology, a set of high-response rate and low-response rate sensors is employed in a computational model. As a consequence of this model, a multi-sensor equipped device has been experimentally setup to conduct measurements on real scenarios. Experiments have been devised to estimate the robustness of the detection. In particular, some preliminary evaluations related to using a minimal set of chemical sensors are reported.

NN-based measurements for driving pattern classification

This paper aims at showing how to classify driving patterns in terms of primitives such as acceleration, deceleration and turning, using neural networks. In particular a multilayer perceptron with back-propagation learning algorithm is used. The considered feature space is reduced to a very restricted couple of sensors: accelerometer and GPS receiver, which characterize many commercial low-cost inertial navigation systems (INS). Sensor-driven input patterns are used for classification over output driving primitives. The ease of this approach holds true since GPS data coupled with forward and lateral accelerations are sufficient for describing much of the semantics of driving scenarios. This argument is supported by real observations on different types of vehicles and different types of drivers. These measurements show that, in normal conditions, the road geometry implies a vehicle to adopt a well-defined behaviour, which can therefore straightforwardly be characterized.