This paper introduces a smart assistant for professional volleyball training based on machine-learning techniques (SAETA). SAETA addresses two main aspects of elite sports coaching: 1) technical-tactical effort control, which aims at controlling exercise effort and fatigue levels and 2) exercise quality training, which complements the former by analyzing the execution of player movements. SAETA relies on a sensing infrastructure that monitors both players and their environment, and produces real-time data that is analyzed by different modules of a decision engine. Technical-tactical effort control is based on a dynamic programming model, which selects the best activity and rest durations in interval training, with the goal of maximizing effort while preventing fatigue. Exercise quality control consists of two stages. In the first stage, movements are detected by means of a k-nearest neighbors classifier and in the second stage, movement intensity is classified according to recent statistical data from the player being analyzed. These analyses are reported to coaches and players in real-time. SAETA has been developed in close collaboration with the Universidad Católica San Antonio de Murcia volleyball team, which competes in the Spanish women's premier league. Data gathered during training sessions has provided a knowledge base for the algorithms developed, and has been used for the validation of results.

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In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabaneros National Park and the SEPRONA service, proposes a system to automatically detect and locate gunshots. This work presents its technical aspects related to network design and planning. The system consists of a network of acoustic sensors that locate gunshots by hyperbolic multi-lateration estimation. The differences in sound time arrivals allow the computation of a low error estimator of gunshot location. The accuracy of this method depends on tight sensor clock synchronization, which an ad-hoc time synchronization protocol provides. On the other hand, since the areas under surveillance are wide, and electric power is scarce, it is necessary to maximize detection coverage and minimize system cost at the same time. Therefore, sensor network planning has two targets, i.e., coverage and cost. We model planning as an unconstrained problem with two objective functions. We determine a set of candidate solutions of interest by combining a derivative-free descent method we have recently proposed with a Pareto front approach. The results are clearly superior to random seeding in a realistic simulation scenario.

Abstract-In this paper we solve analytically the problem of distributing optimally a set of t slots between a group of n readers in RFID dense environments where a single frequency channel is available. In these environments, the readers within reader-to-reader interference range must transmit at different times, otherwise tags cannot identified. This resource allocation problem is addressed for both static and dynamic Frame Slotted Aloha, which are the most broadly extended mechanism used in UHF RFID systems. The goal is maximizing the expected number of tags successfully identified within the t slots. Results demonstrate that the optimal solution outperforms an assignment proportional to the number of tags in each reader. The results heavily depend on the underlying reading algorithm of the reader.

Schedulers in radio frequency identification dense environments aim at distributing optimally a set of t slots between a group of m readers. In single-channel environments, the readers within mutual interference range must transmit at different times; otherwise, interferences prevent identification of the tags. The goal is to maximize the expected number of tags successfully identified within the t slots. This problem may be formulated as a mixed integer non-linear mathematical program, which may effectively exploit available knowledge about the number of competing tags in the reading zone of each reader. In this paper, we present this optimization problem and analyze the impact of tag estimation in the performance achieved by the scheduler. The results demonstrate that optimal solutions outperform a reference scheduler based on dividing the available slots proportionally to the number of tags in each reader. In addition, depending on the scenario load, the results reveal that there exist an optimum number of readers for the topology considered, since the total average number of identifications depend non-linearly on the load. Finally, we study the effect of imperfect tag population knowledge on the performance achieved by the readers.