IEEE 802.21: Media independence beyond handover

The IEEE 802.21 standard facilitates media independent handovers by providing higher layer mobility management functions with common service primitives for all technologies. Right after the base specification was published, several voices rose up in the working group advocating to broaden the scope of IEEE 802.21 beyond handovers. This paper aims at updating the reader with the main challenges and functionalities required to create a Media Independence Service Layer, through the analysis of scenarios which are being discussed within the working group: 1) Wireless Coexistence, and 2) Heterogeneous Wireless Multihop Backhaul Networks.     

Structure and reversibility of 2D hexagonal cellular automata

Cellular automata are used to model dynamical phenomena by focusing on their local behavior which depends on the neighboring cells in order to express their global behavior. The geometrical structure of the models suggests the algebraic structure of cellular automata. After modeling the dynamical phenomena, it is sometimes an important problem to be able to move backwards in order to understand it better. This is only possible if cellular automata is reversible. In this paper, 2D finite cellular automata defined by local rules based on hexagonal cell structure are studied. Rule matrix of the hexagonal finite cellular automaton is obtained. The rank of rule matrices representing the 2D hexagonal finite cellular automata via an algorithm is computed. It is a well known fact that determining the reversibility of a 2D cellular automata is a very difficult problem in general. Here, the reversibility problem of this family of 2D hexagonal cellular automata is also resolved completely.     

Improving traversability of quadruped walking robots using body movement in 3D rough terrains

This paper presents a study on improving the traversability of a quadruped walking robot in 3D rough terrains. The key idea is to exploit body movement of the robot. The position and orientation of the robot are systematically adjusted and the possibility of finding a valid foothold for the next swing is maximized, which makes the robot have more chances to overcome the rough terrains. In addition, a foothold search algorithm that provides the valid foothold while maintaining a high traversability of the robot, is investigated and a gait selection algorithm is developed to help the robot avoid deadlock situations. To explain the algorithms, new concepts such as reachable area, stable area, potential search direction, and complementary kinematic margin are introduced, and the effectiveness of the algorithms is validated via simulations and experiments.     

Water diffusion in polymer nano-films measured with microcantilevers

We present a method to measure the absorption of water molecules from the liquid and the vapour phase into polymer nano-films and the diffusion inside these films. Film thickness can be down to 45 nm. To demonstrate the possibilities of this method we use polymer films that are deposited on the upper side of a silicon cantilever by plasma polymerization of norbornene. When a microdrop of water is deposited onto the initially straight cantilever, the drop causes the cantilever to bend while it evaporates. Evaporation of such small water drops usually takes less than a second. An upwards bending is due to capillary forces and a downwards bending is due to the diffusion of water into the polymer film – and the consequent volume expansion (swelling) of the film. The magnitude of the capillary forces and the extent of swelling continuously change during drop evaporation. When drop evaporation is over the cantilever returns to its initial straight position. We simulate the time dependent bending with a numerical model that qualitatively agrees with the experiment. From the time dependence of cantilever bending we are able to determine the diffusion coefficient of water in the thin polymer film.     

A photoluminescence-based quantum semiconductor biosensor for rapid in situ detection of Escherichia coli

This work describes a novel method of detecting Escherichia coli using photoluminescence (PL) emission from III–V quantum semiconductor (QS) devices functionalized with two different antibody-based architectures. The first approach employed self-assembled monolayers of biotinylated polyethylene glycol thiols to immobilize biotinylated antibody via neutravidin. In the second approach, we used QS microstructures coated with a thin layer of Si₃N₄ allowing direct functionalization with E. coli antibodies through hydrofluoric acid etching and glutaraldehyde-based reticulation. Atomic force, optical and fluorescence microscopy measurements were used to assess the immobilization process. Depending on the biosensing architecture, density of the immobilized bacteria was observed in the range of 0.5–0.7 bacteria/100 μm². The detection of E. coli at 10⁴ CFU/ml was achieved within less than 120 min of the bacteria exposure. It is expected that an even better sensitivity threshold could be achieved following further optimization of the method.     

From embedded sensors to sensorial materials—The road to function scale integration

Ubiquitous computing is about to become part of our everyday lives by integrating hundreds of “invisible” to us computing devices in our environment, so that they can unobtrusively and constantly assist us. This will imply more and smaller “invisible” sensors, homogeneously distributed and at the same time densely packed in host materials, responding to various stimuli and immediately delivering information. In order to reach this aim, the embedded sensors should be integrated within the host material, heading towards sensorial materials. The first step is to omit all parts that are not needed for the sensorial task and to find new solutions for a gentle integration. This is what we call function scale integration. The paper discusses sensor embedding in the human hand as an example of integration in nature, new technological applications and main challenges associated with this approach.     

Characterization of microdevices for ferrous chloride separation for biosensing applications

Ferrous chloride has a variety of applications such as a reducing flocculation agent in waste-water treatment, especially for wastes containing chromate, in the laboratory synthesis of iron complexes and it is employed as a reducing agent in many organic syntheses. The device used for experiment was fabricated on the silicon wafer as support for two electrodes in a SU8 polymer microchannel with an inlet, for the injection of aqueous solution of ferrous chloride, and two outlets, for the two by-products of separated solutions. The various parameters of the device were measured by White Light Interferometer (WLI) and Scanning Electron Microscopy (SEM). The magnetic field created by applying different types of potential between two electrodes determined ferrous chloride to separate in ferrous oxide and chlorine (in gaseous form). If a protein is added in this solution we have the possibility to immobilize the protein on the iron particles and on the channel area. The electrical results were collected using a semiconductor system analyzer Keithley and were examined subsequently. The Fe complexes deposited on the electrodes were characterized by XRD analyses.     

Gaussian kernel optimization: Complex problem and a simple solution

The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use.     

Neural network based controller for Cr–Fe batch reduction process

An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr⁶⁺–Fe²⁺ reduction process. Simulated data from the Cr⁶⁺–Fe²⁺ model derived are validated with online data and laboratory analysis using ICP-AES analysis method. The distinctive trend or patterns exhibited in the ORP profiles for the non-equilibrium model derived have been utilized to train neural network-based controllers for the process. The implementation of this process control is to ensure sufficient Fe²⁺ solution is dosed into the wastewater sample in order to reduce all Cr⁶⁺–Cr³⁺. The neural network controller has been utilized to compare the capability of set-point tracking with a PID controller in this process. For this process neural network-based controller dosed in less Fe²⁺ solution compared to the PID controller which hence reduces wastage of chemicals. Industrial Cr⁶⁺ wastewater samples obtained from an electro-plating factory has also been tested on the pilot plant using the neural network-based controller to determine its effectiveness to control the reduction process for a real plant. The results indicate the proposed controller is capable of fully reducing the Cr⁶⁺–Cr³⁺ in the batch treatment process with minimal dosage of Fe²⁺.