Francisco J. González-Castaño

Integration of nomadic devices with automotive user interfaces

The number of nomadic devices such as PDAs, MP3 players and especially cell phones has largely grown in the last years; Indeed, cell phones are becoming powerful, always-connected computers that store a lot of personal information (contacts, calendar, personal videos and pictures). The small size of their keyboards and screens is a major limitation of nomadic devices, hampering their utilization in vehicles. Handling them while driving may distract the user and increases the risk of accident, and it is illegal in some countries. According to the Web 2.0 vision, the Internet is a platform rather than a mere source of information. The interfaces of Ajax-based applications are user friendly, and they allow storing personal information and preferences. Google Maps is a good example of this philosophy. Nowadays, it is a familiar tool in desktop computers to locate destinations and to plan routes at home. In this paper we propose a new model of Web 2.0 information migration to nomadic devices that facilitates is access to cars.

Experiences inside the Ubiquitous Oulu Smart City

The UrBan Interactions (UBI) research program, coordinated by the University of Oulu, has created a middleware layer on top of the panOULU wireless network and opened it up to ubiquitous-computing researchers, offering opportunities to enhance and facilitate communication between citizens and the government.

Multiframe Maximum-Likelihood Tag Estimation for RFID Anticollision Protocols

Automatic identification based on radio frequency identification (RFID) is progressively being introduced into industrial environments, enabling new applications and processes. In the context of communications, RFID rely mostly on Frame Slotted Aloha (FSA) anticollision protocols. Their goal is to reduce the time required to detect all the tags within range (identification time). Using FSA, the maximum identification rate is achieved when the number of contending tags equals the number of contention slots available in the frame. Therefore, the reader must estimate the number of contenders and allocate that number of slots for the next frame. This paper introduces the new MFML-DFSA anticollision protocol. It estimates the number of contenders by means of a maximum-likelihood estimator, which uses the statistical information from several frames (multiframe estimation) to improve the accuracy of the estimate. Based on this expected number of tags, the algorithm determines the best frame length for the next reading frame, taking into account the constraints of the EPCglobal Class-1 Gen-2 standard. The MFML-DFSA algorithm is compared with previous proposals and found to outperform these in terms of (lower) average identification time and computational cost, which makes it suitable for implementation in commercial RFID readers.

A Survey on Cooperative Diversity for Wireless Networks

Diversity, i.e. transmitting multiple replicas of a signal, may mitigate fading in wireless networks. Among other diversity techniques, the space diversity of multi-antenna systems is particularly interesting since it can complement other forms of diversity. The recent cooperative diversity paradigm brings the advantages of multi-antenna space diversity to single antenna networked devices, which, through cooperation and antenna sharing, form virtual antenna arrays. However, cooperative diversity is a complex technique and research on this topic is still in its early stages. This paper aims at providing a general survey on the theoretical framework; and the physical and medium access control proposals in the literature.

Bluetooth location networks

We propose a Bluetooth location network (BLN) for location-aware or context-driven mobile networks, such as m-commerce networks or e-museums. We assume that, in any of those scenarios, there exist service servers that need to know user location in real-time, to send context-oriented information to user handhelds when necessary. The BLN transmits position information to the service servers, without user participation. It is not subject to line-of-sight constraints and is supported by existing commercial handhelds. BLN users carry either a Bluetooth-enabled handheld or any mobile data terminal and a Bluetooth badge. The BLN is composed of small, wireless Bluetooth nodes, which establish a spontaneous network topology at system initialization. The BLN can coexist with Bluetooth devices that are not part of the location system, such as printers or headphones.

Condor grid computing from mobile handheld devices

In this paper, we propose a hierarchical design methodology for grid access from handheld devices. After determining all user interactions required and technologies available, they are arranged in layers. All functions in a layer are also supported by all underlying layers. By doing so, the designer is less conditioned by the constraints of a specific, out-of-context platform. Additionally, in a stratified modular design, many software components can be re-used. We present a prototype to access Condor from two neighbor layers: PDAs and cell phones.

Ambient Intelligence Systems for Personalized Sport Training

Several research programs are tackling the use of Wireless Sensor Networks (WSN) at specific fields, such as e-Health, e-Inclusion or e-Sport. This is the case of the project “Ambient Intelligence Systems Support for Athletes with Specific Profiles”, which intends to assist athletes in their training. In this paper, the main developments and outcomes from this project are described. The architecture of the system comprises a WSN deployed in the training area which provides communication with athletes’ mobile equipments, performs location tasks, and harvests environmental data (wind speed, temperature, etc.). Athletes are equipped with a monitoring unit which obtains data from their training (pulse, speed, etc.). Besides, a decision engine combines these real-time data together with static information about the training field, and from the athlete, to direct athletes’ training to fulfill some specific goal. A prototype is presented in this work for a cross country running scenario, where the objective is to maintain the heart rate (HR) of the runner in a target range. For each track, the environmental conditions (temperature of the next track), the current athlete condition (HR), and the intrinsic difficulty of the track (slopes) influence the performance of the athlete. The decision engine, implemented by means of (m, s)-splines interpolation, estimates the future HR and selects the best track in each fork of the circuit. This method achieves a success ratio in the order of 80%. Indeed, results demonstrate that if environmental information is not take into account to derive training orders, the success ratio is reduced notably.

Extending Vehicular CAN Fieldbuses With Delay-Tolerant Networks

In this paper, we propose and evaluate the possibility of extending vehicular controller-area network (CAN) fieldbuses with delay-tolerant networks (DTNs) based on the multihop car2car (C2C)-or vehicle2vehicle-paradigm. Previous research has focused on single-hop wireless communications, which are inadequate in many real-life scenarios [1]. We demonstrate that efficient network layers, such as DTNs, are necessary to overcome those limitations. As a proof of concept, we present a working prototype of a system that extends the standard diagnostics interface of a CAN bus.

Unsupervised method for sentiment analysis in online texts

Method to predict sentiment in informal texts using unsupervised dependency parsing.Algorithm based on sentiment propagation using linguistic content without training.Method to create lexicon using polarity expansion algorithm for specific domains.Our method compares favorably well with other unsupervised and supervised methods. In recent years, the explosive growth of online media, such as blogs and social networking sites, has enabled individuals and organizations to write about their personal experiences and express opinions. Classifying these documents using a polarity metric is an arduous task. We propose a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons. These lexicons were created by means of a semiautomatic polarity expansion algorithm in order to improve accuracy in specific application domains. The results obtained for the Cornell Movie Review, Obama-McCain Debate and SemEval-2015 datasets confirm the competitive performance and the robustness of the system.

A Combined Global & Local Search (CGLS) Approach to Global Optimization

This paper presents a general approach that combines global search strategies with local search and attempts to find a global minimum of a real valued function of n variables. It assumes that derivative information is unreliable; consequently, it deals with derivative free algorithms, but derivative information can be easily incorporated. This paper presents a nonmonotone derivative free algorithm and shows numerically that it may converge to a better minimum starting from a local nonglobal minimum. This property is then incorporated into a random population to globalize the algorithm. Convergence to a zero order stationary point is established for nonsmooth convex functions, and convergence to a first order stationary point is established for strictly differentiable functions. Preliminary numerical results are encouraging. A Java implementation that can be run directly from the Web allows the interested reader to get a better insight of the performance of the algorithm on several standard functions. The general framework proposed here, allows the user to incorporate variants of well known global search strategies.