Inkjet printed LED based pH chemical sensor for gas sensing

Predictable behaviour is a critical factor when developing a sensor for potential deployment within a wireless sensor network (WSN). The work presented here details the fabrication and performance of an optical chemical sensor for gaseous acetic acid analysis, which was constructed using inkjet printed deposition of a colorimetric chemical sensor. The chemical sensor comprised a pH indicator dye (bromophenol blue), phase transfer salt tetrahexylammonium bromide and polymer ethyl cellulose dissolved in 1-butanol. A paired emitter-detector diode (PEDD) optical detector was employed to monitor responses of the colorimetric chemical sensor as it exhibits good sensitivity, low power consumption, is low cost, accurate and has excellent signal to noise ratios. The chemical sensor formulation was printed directly onto the surface the emitter LED, and the resulting chemical sensors characterised with respect to their layer thickness, response time and recovery time. The fabrication reproducibility of inkjet printed chemical sensors in comparison to drop casted chemical sensors was investigated. Colorimetric chemical sensors produced by inkjet printing, exhibited an improved reproducibility for the detection of gaseous acetic acid with a relative standard deviation of 5.5 % in comparison to 68.0 % calculated for drop casted sensors (n = 10). The stability of the chemical sensor was also investigated through both intra and inter-day studies

Flexible format, computer accessed telemetry system

With this system, it is possible to sample and generate two or more simultaneous formats; one can be transmitted to ground station in real time, and other is stored for later transmission. Sensor output comparison data, plus information to control format, compression algorithm, and allowable degree of sensor activity, are stored in memory

Benefits from remote sensing data utilization in urban planning processes and system recommendations

The benefits of utilizing remote sensor data in the urban planning process of the Metropolitan Washington Council of Governments are investigated. An evaluation of sensor requirements, a description/ comparison of costs, benefits, levels of accuracy, ease of attainment, and frequency of update possible using sensor versus traditional data acquisition techniques are discussed

Geometric Cross-Modal Comparison of Heterogeneous Sensor Data

In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at various locations. The information represented by the two modalities is compared using self-similarity matrices (SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. Several metrics for comparing SSMs are explored, including a cutting-edge time-warping technique that can simultaneously handle local time warping and partial matches, while also controlling for the change in geometry between feature spaces of the two modalities. We note that this technique is quite general, and does not depend on the choice of modalities. In this particular setting, we demonstrate that the cross-modal distance between SSMs corresponding to the same trajectory type is smaller than the cross-modal distance between SSMs corresponding to distinct trajectory types, and we formalize this observation via precision-recall metrics in experiments. Finally, we comment on promising implications of these ideas for future integration into multiple-hypothesis tracking systems.Comment: 10 pages, 13 figures, Proceedings of IEEE Aeroconf 201

Giving Neurons to Sensors: An Approach to QoS Management Through Artificial Intelligence in Wireless Networks

For the latest ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, selforganizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore the performance of two very well known routing paradigms, directed diffusion and Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

Medium Access Control for Wireless Sensor Networks based on Impulse Radio Ultra Wideband

This paper describes a detailed performance evaluation of distributed Medium Access Control (MAC) protocols for Wireless Sensor Networks based on Impulse Radio Ultra Wideband (IR-UWB) Physical layer (PHY). Two main classes of Medium Access Control protocol have been considered: Slotted and UnSlotted with reliability. The reliability is based on Automatic Repeat ReQuest (ARQ). The performance evaluation is performed using a complete Wireless Sensor Networks (WSN) simulator built on the Global Mobile Information System Simulator (GloMoSim). The optimal operating parameters are first discussed for IR-UWB in terms of slot size, retransmission delay and the number of retransmission, then a comparison between IR-UWB and other transmission techniques in terms of reliability latency and power efficiency