This work demonstrates the improvement of mass detection sensitivity and time response using a quite simple structure of sensor. Indeed, complicated technological processes are often required to reach high sensitivity when we want to detect specific molecules in biological fields. These developments constitute an obstacle to the early diagnosis of diseases. An alternative is the design of coupled structures. The device is based on the piezoelectric excitation and detection of two GaAs micro structures vibrating on antisymmetric modes. GaAs is a piezoelectric material which has the advantage to be micromachined easily using clean room processes. Moreover, we showed its high potential in direct biofunctionalisation to be used in biological field¹. A specific design of electrodes (three electrodes) was performed to improve the detection at low mass and an original detection method has been developed. The principle is to exploit the variation in amplitude at a fixed frequency when we are concerned by a weak frequency shift of the resonance peak. The three electrodes are geometrically identical. We excited the device at the resonance frequency, corresponding to maximum voltage of the initialization electrode. Thus we noted the voltage on the measuring electrode, which had, in the vicinity of weak added mass, the greatest slope. Therefore, we get a very good resolution for an infinitely weak mass: relative voltage variation of 8%/1fg. The analysis given in this paper is based on results obtained by finite element modeling.     ¹A. Bienaime et al, Materials 2013, 6, 4946-4966

3D scanner captures the appearance and geometry of a real object, and forms a virtual one which can be displayed in 3D on computers. However, the high cost of 3D scanning devices inhibits the popularity of scanned 3D objects on the web and related applications. Our system targets for developing a consumer-level 3D scanning device suitable for naïve web users with the use of a novel natural depth sensing device – Kinect. As the Kinect device is a low-cost, robust and fast depth camera with color, these essential features enable us to acquire RGBD images from different views of the target object. To facilitate the registration of point clouds from various depth images, we first combine marker-based tracking system to estimate the current view angle of Kinect during capture. Then, we employ the Iterative Closest Point (ICP) algorithm with the efficiency feature detection to reconstruct a single point cloud representing the 3D object surface. To reduce size of point cloud, a down-sampling is applied to remove redundant and densely sampled points in the model. The whole process of capturing, storing, uploading and displaying 3D data on web application is done with minimal user involvements. Users can freely control the viewing angle of their favorite product with the 3D scanned point cloud object displayed in the browser; this greatly enhances user's shopping experience. We believe that the introduction of attractive and fashionable 3D product on the web will gain significant attention from customers and evolve habits and traditions in e-commerce, especially for C2C business, e.g. auction sites like ebay and taobao, in which sellers can promote their goods for sale with scanned 3D models

In layered protocol architectures, direct interactions between network protocols in non-adjacent layers are prohibited to maintain the modularity of protocol designs. However, recent research has shown that allowing information to be directly exchanged between layers (a.k.a cross-layer interaction) can optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing schemes on cross-layer optimization mainly involve information exchange between physical, medium access control (MAC), and routing layers, with only a handful involving application layer. In this paper, we examined the issue of cross-layer optimization in WSNs deployed for ambient intelligence (AmI) applications. In AmI, WSNs perform human-centric sensing where low-level sensor data on users and their surroundings are collated and processed to infer higher-level user context information for context-adaptive AmI applications. For the first time, this paper proposes a cross-layer optimization scheme based on AmI context at the application layer for WSNs. As part of this cross-layer scheme, an ontology-based context modelling and reasoning mechanism is also proposed. We applied the proposed cross-layer scheme and context mechanisms to adapt the backoff behaviour of a contention-based WSN MAC protocol to AmI contexts. Results show that the AmI context-aware MAC protocol with cross-layer interaction can yield appreciable performance improvement in terms of throughput, frame delay, and energy efficiency.

Seabed classification is the important part of current coastal research because it characterizes the seabed and its habitats. Seabed characterization makes the link between the classified area and the physical, geological, chemical or biological properties of seabed. This paper addresses the possibilities of the use of airborne remote sensing with a CASI-1500 hyperspectral sensor to map the coverage and the topography of seabed in the western coastal part of Korea. From April to October in 2012, hyperspectral imagery was acquired at low tide. After radiometric, geometric and atmospheric correction for the raw images, the classification was performed in three steps. Firstly, ten classes of seabed were identified using a supervised spectral angle mapping algorithm in combination with data collected by field survey. Secondly, seabed mapping was performed for each class separately using spectral and spatial information. Finally, an accuracy assessment of the mapping results was performed using data from field survey. The overall accuracy was 83% with a kappa coefficient of 0.76. The results indicated that the hyperspectral sensing can help not only to classify the seabed material remotely and precisely, but also to construct the continuous geographical information for an effective management and conservation of the coastal area in Korea.

In this study, a flexible sensor module was developed that can be applied in the activation training of spinal muscles. Silver films were sputtered onto flexible substrates to produce a flexible sensor. Assuming that spinal muscle elongation is positively correlated with the variations in skin surface length, real-time resistance changes exhibited by the sensor during simulated trainings were measured. The results were used to identify the relationship between resistance change and skin surface length (the dependent variable). In addition, ultrasound measurements and electromyography were used to verify the feasibility of the proposed sensor. Furthermore, the developed module can facilitate assessments of the movement accuracy of users during training. Inaccurate movements cause drastic resistance changes in the films of the flexible substrates, and the results are displayed on a screen. Thus, people using the developed sensor in spinal muscle activation training can adjust their posture to the appropriate position.