In retrieval from image databases, evaluation of similarity, based both on the appearance of spatial entities and on their mutual relationships, depends on content representation based on attributed relational graphs. This kind of modeling entails complex matching and indexing, which presently prevents its usage within comprehensive applications. In this paper, we provide a graph-theoretical formulation for the problem of retrieval based on the joint similarity of individual entities and of their mutual relationships and we expound its implications on indexing and matching. In particular, we propose the usage of metric indexing to organize large archives of graph models, and we propose an original look-ahead method which represents an efficient solution for the (sub)graph error correcting isomorphism problem needed to compute object distances. Analytic comparison and experimental results show that the proposed lookahead improves the state-of-the-art in state-space search methods and that the combined use of the proposed matching and indexing scheme permits for the management of the complexity of a typical application of retrieval by spatial arrangement.

In the verification of reactive systems with nondeterministic densely valued temporal parameters, the state-space can be covered through equivalence classes, each composed of a discrete logical location and a dense variety of clock valuations encoded as a difference bounds matrix (DBM). The reachability relation among such classes enables qualitative verification of properties pertaining events ordering and stimulus/response deadlines, but it does not provide any measure of probability for feasible behaviors. We extend DBM equivalence classes with a density-function which provides a measure for the probability of individual states. To this end, we extend time Petri nets by associating a probability density-function to the static firing interval of each nondeterministic transition. We then explain how this stochastic information induces a probability distribution for the states contained within a DBM class and how this probability evolves in the enumeration of the reachability relation among classes. This enables the construction of a stochastic transition system which supports correctness verification based on the theory of TPNs, provides a measure of probability for each feasible run, enables steady-state analysis based on Markov renewal theory. In so doing, we provide a means to identify feasible behaviors and to associate them with a measure of probability in models with multiple concurrent generally distributed nondeterministic timers.

Automated monitoring and the recognition of activities of daily living (ADLs) is a key challenge in ambient-assisted living (AAL) for the assistance of the elderly. Within this context, a formal approach may provide a means to fill the gap between the low-level observations acquired by sensing devices and the high-level concepts that are required for the recognition of human activities. We describe a system named ARA (Automated Recognizer of ADLs) that exploits propositional temporal logic and model checking to support automated real-time recognition of ADLs within a smart environment. The logic is shown to be expressive enough for the specification of realistic patterns of ADLs in terms of basic actions detected by a sensorized environment. The online model checking engine is shown to be capable of processing a stream of detected actions in real time. The effectiveness and viability of the approach are evaluated within the context of a smart kitchen, where different types of ADLs are repeatedly performed.

Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.