Location-aware computing: a neural network model for determining location in wireless LANs

The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented

Simple and complex behavior learning using behavior hidden Markov model and CobART

This paper proposes behavior learning and generation models for simple and complex behaviors of robots using unsupervised learning methods. While the simple behaviors are modeled by simple-behavior learning model (SBLM), complex behaviors are modeled by complex-behavior learning model (CBLM) which uses previously learned simple or complex behaviors. Both models include behavior categorization, behavior modeling, and behavior generation phases. In the behavior categorization phase, sensory data are categorized using correlation based adaptive resonance theory (CobART) network that generates motion primitives corresponding to robot's base abilities. In the behavior modeling phase, a modified version of hidden Markov model (HMM), is called Behavior-HMM, is used to model the relationships among the motion primitives in a finite state stochastic network. At the same time, a motion generator which is an artificial neural network (ANN) is trained for each motion primitive to learn essential robot motor commands. In the behavior generation phase, a motion primitive sequence that can perform the desired task is generated according to the previously learned Behavior-HMMs at the higher level. Then, in the lower level, these motion primitives are executed by the motion generator which is specifically trained for the corresponding motion primitive. The transitions between the motion primitives are done according to observed sensory data and probabilistic weights assigned to each transition during the learning phase. The proposed models are not constructed for one specific behavior, but are intended to be bases for all behaviors. The behavior learning capabilities of the model is extended by integrating previously learned behaviors hierarchically which is referred as CBLM. Hence, new behaviors can take advantage of already discovered behaviors. Performed experiments on a robot simulator show that simple and complex-behavior learning models can generate requested behaviors effectively