Electrospun granular hollow SnO2 nanofibers hydrogen gas sensors operating at low temperatures

In this paper, we present H2 gas sensors based on hollow and filled, well-aligned electrospun SnO2 nanofibers, operating at a low temperature of 150 C. SnO2 nanofibers with diameters ranging from 80 to 400 nm have been successfully synthesized in which the diameter of the nanofibers can be controlled by adjusting the concentration of polyacrylonitrile in the solution for electrospinning. The presence of this polymer results in the formation of granular walls for the nanofibers. We discussed the correlation between nanofibers morphology, structure, oxygen vacancy contents and the gas sensing performances. X-ray photoelectron spectroscopy analysis revealed that the granular hollow SnO2 nanofibers, which show the highest responses, contain a significant number of oxygen vacancies, which are favorable for gas sensor operating at low temperatures. © 2014 American Chemical Society.

Remote monitoring system enabling cloud technology upon smart phones and inertial sensors for human kinematics

Stroke is a common neurological condition which is becoming increasingly common as the population ages. This entails healthcare monitoring systems suitable for home use, with remote access for medical professionals and emergency responders. The mobile phone is becoming the easy access tool for self-evaluation of health, but it is hindered by inherent problems including computational power and storage capacity. This research proposes a novel cloud based architecture of a biomedical system for a wearable motion kinematic analysis system which mitigates the above mentioned deficiencies of mobile devices. The system contains three subsystems: 1. Bio Kin WMS for measuring the acceleration and rotation of movement 2. Bio Kin Mobi for Mobile phone based data gathering and visualization 3. Bio Kin Cloud for data intensive computations and storage. The system is implemented as a web system and an android based mobile application. The web system communicates with the mobile application using an encrypted data structure containing sensor data and identifiable headings. The raw data, according to identifiable headings, is stored in the Amazon Relational Database Service which is automatically backed up daily. The system was deployed and tested in Amazon Web Services.

Placing sensors for area coverage in a complex environment by a team of robots

Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an "entrance" to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40-50% of the ROI, whereas BTD covers it in full. BTD involves significantly (80%) less robot moves and messages than LRV.

Graphene and graphitic derivative filled polymer composites as potential sensors

Graphite and numerous graphitic-derived micro- and nano-particles have gained importance in current materials science research. These two-dimensional sheets of sp(2)-hybridized carbon atoms remarkably influence the properties of polymers. Graphene mono-layers, graphene oxides, graphite oxides, exfoliated graphite, and other related materials are derived from a parental graphite structure. In this review, we focus primarily on the role of these fillers in regulating the electrical and sensing properties of polymer composites. It has been demonstrated that the addition of an optimized mixture of graphene and or its derivatives to various polymers produces a record-high enhancement of the electrical conductivity and achieved semiconducting characteristics at small filler loading, making it suitable for sensor manufacture. Promising sensing characteristics are observed in graphite-derived composite films compared with those of micro-sized composites and the properties are explained mainly based on the filler volume fraction, nature and rate of dispersion and the filler polymer interactions at the interface. In short, this critical review aims to provide a thorough understanding of the recent advances in the area of graphitic-based polymer composites in advanced electronics. Future perspectives in this rapidly developing field are also discussed.

Inference of personal sensors in the internet of things

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.