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      Sensors in Collaboration Increase Individual Potentialities

      editorial
      Sensors (Basel, Switzerland)
      Molecular Diversity Preservation International (MDPI)

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          Abstract

          Different applications require different sensor technologies and methods to achieve specific goals. Particular sensor designs are focused on solving problems. It is well-known that individual sensors can be limited when complex problems or applications are involved or the application requires sensing in different locations or even different geographical areas. We could think of robotic applications where vision, ultrasounds or tactile technologies among others are considered as a whole with the goal of navigation and exploration. Individual sensors are insufficient for achieving the goal, but in collaboration the objective can be achieved and even with high effectiveness. Some sensor devices are arrays of single elements, such as tactile or electronic-noses. In both cases, sensors are related to a specific location. On the contrary, some applications are based on the distribution of sensors at different locations, interconnected under a network for collaboration. At each specific location different sensors can be working in collaboration. Works on this special issue cover the topic of collaborative sensors under different configurations, always enhancing and improving performances of individual sensors, based on the fusion of the information provided by the different sensors. Different categories and specific applications are considered, where each work is assigned to one or more specific categories and listed in the appropriate references section. Wireless Sensor Networks (WSN) (1) coverage precedence routing algorithm, ensuring full functionality, for quality of service in WSN, [1]; (2) Diffusion-based Expectation-Maximization algorithm for energy-efficient solution in WSN [2]; (3) trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm for multiple event source localization using binary information from the sensor nodes in WSN [3]; (4) collaborative localization algorithms for nodes in WSN without GPS [4]; (5) prediction (data not sent to the sink node) accuracy for data reduction in WSN [5]; (6) grid-based distributed event detection scheme for WSN [6]; (7) WSNs for intelligent transportation systems [7]; remote testbed with WSN and mobile robots equipped with a set of low-cost off-the-shelf sensors for cooperative perception [8]; (8) wireless body area networks for monitoring health parameters are useful for transmitting data externally [9]; (9) distributed and formula-based bilateration algorithm used to provide initial set of locations in WSN [10]; (10) Artificial neural network to estimate the location of a mobile station in wireless communication systems [11]; (11) WSN and minimax method in early detection to neutralize intruders in strategic installations [12]. Medicine and Health Services (1) wireless wearable and ambient sensors that cooperate to monitor person's vital signs such as heart rate and blood pressure during daily activities [13]; (2) body sensor networks with wireless technology can be used for the acquisition of health related information, which is transmitted to an external gateway, such as a PDA [14] Inertial Measurement Units (1) fusion algorithms for using multiple Inertial Measurement Unit (IMUs) to enhance performance in the context of pedestrian navigation [14]; (2) a set of distributed accelerometers are arranged and integrated as an IMU [15]. Micro-Electro-Mechanical Systems (MEMS) (1) based on low-cost sensors along buried pipes in communication with a smart server for decision making [16]; (2) body sensor networks for health purposes [9]. Security in Intelligent Sensors patterns-based security specifications and new ontological specification [17]. Oceanographic and Meteorological instruments are installed on a buoy as a multisensory moored platform for continuous and autonomous monitoring of the pelagic system in Western Mediterranean [18]. Robotics (1) Odometry and laser scanners are integrated for relative localization for navigation of a convoy of robotic units in indoor environments [19]; (2) autonomous robot-arm model for object manipulation in semi-structured environments based on an intelligent multi-sensor system [20]; (3) specific tasks are distributed and allocated to each element in a swarm robotics by applying optimization methods, such as genetic algorithms [21]; (4) social odometry, where robots learn from the others, based on cooperative reputation systems [22]; (5) 3D parallel mechanism robot-arm with three pneumatic actuators combined with a stereo vision system is developed for path tracking control [23]; (6) remote testbed with mobile robots and WSN equipped with a set of low-cost off-the-shelf sensors in cooperative perception, that present high degree of heterogeneity in their technology, sensed magnitudes, features, output bandwidth, interfaces and power consumption [8]. Automatic House heterogeneous collaborative sensor networks for electrical and energy management on a self-sufficient solar house [24]. Gyroscope a design of force to rebalance control for a hemispherical resonator gyro (HRG) based on FPGA [25]. 3D Structure (1) fusion of stereovision and range finder sensors applied to autonomous vehicles guidance [26];(2) fusion of Kinect™ with laser sensors for reducing limitations of the first [27]. Brain-Computer Interface a hardware and software communication system that permits to control computers and external devices through cerebral activity, specifically appropriate for severely disable people [28]. Surveillance and Tracking applied to detect ground targets through sensor nodes in a distributed network [29].

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          Brain Computer Interfaces, a Review

          A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
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            A Comprehensive Approach to WSN-Based ITS Applications: A Survey

            In order to perform sensing tasks, most current Intelligent Transportation Systems (ITS) rely on expensive sensors, which offer only limited functionality. A more recent trend consists of using Wireless Sensor Networks (WSN) for such purpose, which reduces the required investment and enables the development of new collaborative and intelligent applications that further contribute to improve both driving safety and traffic efficiency. This paper surveys the application of WSNs to such ITS scenarios, tackling the main issues that may arise when developing these systems. The paper is divided into sections which address different matters including vehicle detection and classification as well as the selection of appropriate communication protocols, network architecture, topology and some important design parameters. In addition, in line with the multiplicity of different technologies that take part in ITS, it does not consider WSNs just as stand-alone systems, but also as key components of heterogeneous systems cooperating along with other technologies employed in vehicular scenarios.
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              Data Fusion Algorithms for Multiple Inertial Measurement Units

              A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter’s performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                2012
                13 April 2012
                : 12
                : 4
                : 4892-4896
                Affiliations
                Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain; E-Mail: pajares@ 123456fdi.ucm.es ; Tel.: +34-1-394-7546; Fax: +34-1-394-7547
                Article
                sensors-12-04892
                10.3390/s120404892
                3355447
                22666065
                c1ac1cad-490a-46e8-bb72-c38e0a567d47
                © 2012 by the authors; licensee MDPI, Basel, Switzerland

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 6 April 2012
                : 12 April 2012
                Categories
                Editorial

                Biomedical engineering
                Biomedical engineering

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