Juan-Pablo García-Vázquez

Bio: Juan-Pablo García-Vázquez is an academic researcher from Autonomous University of Baja California. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 8, co-authored 23 publications receiving 499 citations. Previous affiliations of Juan-Pablo García-Vázquez include Monterrey Institute of Technology and Higher Education.

Evolution of Indoor Positioning Technologies: A Survey

Abstract: Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.

Magnetic Field Feature Extraction and Selection for Indoor Location Estimation

Abstract: User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user’s location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.

Supporting the strategies to improve elders' medication compliance by providing ambient aids

Abstract: In this paper, we present Ambient Information Systems (AIS) that support strategies relevant to enable elders to effectively manage their medication, such as: remind (Remind-Me AIS), guide (GUIDE-Me AIS), and motivate (CARE-Me AIS) them to medicate. We have informed the AIS design through a case study we carried out to understand elders' deficiencies for adhering to their medication routine. As a result of the case study and the AIS design process; we identified the design issues that should be addressed when developing AIS that cope with the elders needs for living independently. Identifying these design issues is a step toward proposing design guidelines for the development of AIS for elderly. Through a heuristic evaluation, we identified several usability problems that enabled us to improve AIS characteristics, such as the intuitive mapping of the information representations and the visibility of the different AIS states.

Date Palm Pollen: Features, Production, Extraction and Pollination Methods

Abstract: Date palm pollen (DPP) plays a very important role in the fertilization process, since its viability and the pollination method influence on the quality, development, and yield of the fruit. In the present study, a broad review of its main characteristics, consumption, and DPP production are presented, as well as a description of its extraction methods and viability tests. The evolution of the pollination methods used in the date palm is also presented, from its natural pollination to the use of specialized mechanical and electrical devices, as well as the use of dry DPP and the current trend towards the use of DPP in liquid suspension. Likewise, the efficiency of the methods of natural pollination (wind); traditional (strands placement); dusting hand; dusting with manual, mechanical, or electric pollinator; and liquid pollination were evaluated from the fruit set percentage (FSP). Finally, starting from a scientometric analysis, the pollination methods were widely discussed, concluding that the dusting spraying of pollen suspension with liquid DPP is the pollination method that commonly presents the highest FSP, followed by dusting dry DPP with a motorized pollinator.

An Agent-based Architecture for Developing Activity- Aware Systems for Assisting Elderly

Abstract: Ageing is a global phenomenon which has motivated many research and development projects with the aim of providing computing services that support the active and independent living of the elderly. To integrate the ambient intelligence (AmI) vision into the home environment to allow elders to "age in place", it has been identified the necessity of providing high-level software support for creating ambient assisted living (AAL) environments. We propose activity-aware computing to allow smart environments to provide continuous activity awareness and opportunistically offer assistance aimed at supporting the elders' current activity. This new paradigm calls for novel tools to help developers mirror human activities in the digital domain, and adapt smart environments based on the activities executed by the users. This paper proposes the use of autonomous agents to cope with the design issues for developing activity-aware systems. We specialized the SALSA agent architecture by incorporating customizable activity-aware mechanisms to infer and represent activities. We illustrate the capabilities offered by SALSA autonomous agents through a design of an activity-aware application for helping elders to manage their medication activity.

Evolution of Indoor Positioning Technologies: A Survey

Abstract: Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.

Activity recognition using hybrid generative/discriminative models on home environments using binary sensors.

Abstract: Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network) and SVM (Support Vector Machines), within the framework of HMM (Hidden Markov Model) in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0.05, proving that the hybrid approach is better suited for the addressed domain.

A review of feature selection techniques in structure learning

Abstract: In the last two decades, there has been significant advancement in heuristics for inducing Bayesian belief networks for the purpose of automatic distillation of knowledge from masses of data with target concepts. However, there are various circumstances where we are confronted to fix a set of most influencing variables in modelling of class variable. This arises in provision of confidence measures on set of variables used in the structure learning of data. In this study, we have tweaked empirical as well as theoretical aspects of various feature selection evaluators, their corresponding searching methods under six well known scoring functions in K2 which is a notable structure learning technique in Bayesian belief network. We have come up with some useful findings for overall computationally efficient approach among eleven evaluators. This analysis is useful in inducing better structure from the given dataset in imparting improved performance metric for Bayesian belief network.