Magnetic alignment of cellulose nanowhiskers in an all-cellulose composite

Unidirectional reinforced nanocomposite paper was fabricated from cellulose nanowhiskers and wood pulp under an externally applied magnetic field. A 1.2 Tesla magnetic field was applied in order to align the nanowhiskers in the pulp as it was being formed into a sheet of paper. The magnetic alignment was driven by the characteristic negative diamagnetic anisotropy of the cellulose nanowhiskers. ESEM micrographs demonstrated unidirectional alignment of the nanowhiskers in the all-cellulose composite paper. Comparing with control paper sheets made from wood pulp only, the storage modulus in the all-cellulose nanocomposites increased dramatically. The storage modulus along the direction perpendicular to the magnetic field was much stronger than that parallel to the magnetic field. This new nanocomposite, which contains preferentially oriented microstructures and has improved mechanical properties, demonstrates the possibility of expanding the functionality of paper products and constitutes a promising alternative to hydrocarbon based materials and fibers.     

On the gas pressure forming of aluminium foam sandwich panels: Experiments and numerical simulations

Forming of light-weight highly stiff aluminium foam sandwich (AFS) panels into complex 3D components would mark a development in the manufacturing of these materials. In this work, gas pressure forming of AFS panels is investigated experimentally and using numerical simulations. Deformation behaviour of AFS panels is studied during high-temperature uniaxial tension and compression, and constitutive models are developed and incorporated into FE simulations of the gas pressure forming process. Simulation results and experimental observations show reasonable agreement and demonstrate the possibility of forming AFS panels to significant deformations while maintaining considerable core porosity.     

A model for improved solar irradiation measurement at low flux

Accurate measurement of solar radiation heat flux is important in characterizing the performance of CSP plants. Thermopile type Heat Flux Sensors (HFSs) are usually used for this purpose. These sensors are typically reasonably accurate at high heat fluxes. However measurement accuracy drops significantly as the measured radiation is below 1 kW/m², this often leads to underestimation of the actual flux. At the Masdar Institute Beam Down Solar Thermal Concentrator (BDSTC), measurement of fluxes ranging from 0 kW/m² to more than 100 kW/m² is required. To improve the accuracy of the sensors in the lower range around 1 kW/m², we have performed a test under ambient (not-concentrated) sunlight. Such low irradiation levels are experienced in characterizing the concentration quality of individual heliostats. It was found during the test that the measurement at this low range is significantly affected by ambient conditions and transients in the HFS cooling water temperature. A Root Mean Square Error (RMSE) of more than 100 W/m² was observed even though we kept the transients in water temperature to a minimum. Hence we devised a model to account for this measurement error at this flux range. Using the proposed model decreased the RMSE to less than 10 W/m². The application of the model on existing heat flux measurement installations is facilitated by the fact that it only employs easily measurable variables. This model was checked by using a test data set and the results were in good agreement with the training data set.     

MAC Protocol for Opportunistic Cognitive Radio Networks with Soft Guarantees

Cognitive radio (CR) is the key enabling technology for an efficient dynamic spectrum access. It aims at exploiting an underutilized licensed spectrum by enabling opportunistic communications for unlicensed users. In this work, we first develop a distributed cognitive radio MAC (COMAC) protocol that enables unlicensed users to dynamically utilize the spectrum while limiting the interference on primary (PR) users. The main novelty in COMAC lies in not assuming a predefined CR-to-PR power mask and not requiring active coordination with PR users. COMAC provides a statistical performance guarantee for PR users by limiting the fraction of the time during which the PR users' reception is negatively affected by CR transmissions. To provide such a guarantee, we develop probabilistic models for the PR-to-PR and the PR-to-CR interference under a Rayleigh fading channel model. From these models, we derive closed-form expressions for the mean and variance of interference. Empirical results show that the distribution of the interference is approximately lognormal. Based on the developed interference models, we derive a closed-form expression for the maximum allowable power for a CR transmission. We extend the min-hop routing to exploit the available channel information for improving the perceived throughput. Our simulation results indicate that COMAC satisfies its target soft guarantees under different traffic loads and arbitrary user deployment scenarios. Results also show that exploiting the available channel information for the routing decisions can improve the end-to-end throughput of the CR network (CRN).     

A Micropower Adaptive Linear Transform Vector Quantiser

Micropower signal classification and compression are becoming important requirements for implantable cardioverter defibrillators although they are currently limited by power and computational constraints. This paper describes an integrated circuit that facilitates Intracardiac Electrogram (ICEG) classification and compression of 30 dimensional analogue vectors while consuming a maximum of 2.5 W power for a heart rate of 60 beats per minute (1 vector per second) from a 3.3 V supply. This represents a significant advance on previous work which achieved ultra low power supervised morphology classification [6] since the templated matching scheme used in this chip enables unsupervised blind classification of abnormal rhythms and the computational support for low bit rate data compression. The adaptive template matching scheme used is tolerant to amplitude variations, and inter- and intra-sample time shifts. Micropower performance is achieved using CMOS analogue circuits biased in weak inversion in order to minimise energy per computation. Results from the fabricated chip demonstrate the impact of the amplitude and shift tolerance on ICEG data and performance for blind classification of an abnormal rhythm in five heart patients. For four out of the five patients, no false negative classifications and a worst case of 11% false positive classifications were made.     

Characteristics of a high brightness gaseous field ion source employing tungsten-carbon doped NiAl needles

We report on the characterization of a high brightness gaseous field ion source using an emitter made of a NiAl needle containing tiny spherical tungsten-carbon precipitates. By field evaporation of such a multiphase alloy, a surface protrusion is formed out of a precipitate, which can act as a small source size field ion emitter. The emission current-voltage characteristics of this emitter were recorded for a variety of parameters. The results obtained suggest that its application as a stable ion source is possible even on long term operation.     

Energy-efficient power/rate control and scheduling in hybrid TDMA/CDMA wireless sensor networks

We consider a hybrid TDMA/CDMA wireless sensor network (WSN) and quantitatively investigate the energy efficiency obtained by combining adaptive power/rate control with time-domain scheduling. The energy efficiency improvement is carried out with respect to interfering-cluster scheduling, intra-cluster node scheduling, and transmission powers and times (durations) control (PTC) for individual nodes. The interfering-cluster scheduling is formulated as a vertex-coloring problem, which can be solved efficiently using existing numerical algorithms in graph theory. For the node scheduling problem, we present a heuristic algorithm, which iteratively searches for the best schedule in such a way that the energy consumption keeps decreasing after every iteration. Compared with the exponentially complicated exhaustive search algorithm, this heuristic algorithm has polynomial computing complexity and can provide optimal solutions in the most simulated cases. For the transmission power/time control, two simplified PTC schemes, namely, PTC-UT and PTC-USG, are proposed and studied based on our previous optimization work PTC-IPT. We show that PTC-UT and PTC-USG provide comparable energy efficiency to PTC-IPT at only half of its complexity. Numerical examples are used to validate our findings.     

Parallel Fiber Coding in the Cerebellum for Life-Long Learning

Continuous and real-time learning is a difficult problem in robotics. To learn efficiently, it is important to recognize the current situation and learn appropriately for that context. To be effective, this requires the integration of a large number of sensorimotor and cognitive signals. So far, few principles on how to perform this integration have been proposed. Another limitation is the difficulty to include the complete contextual information to avoid destructive interference while learning different tasks.We suggest that a vertebrate brain structure important for sensorimotor coordination, the cerebellum, may provide answers to these difficult problems. We investigate how learning in the input layer of the cerebellum may successfully encode contextual knowledge in a representation useful for coordination and life-long learning. We propose that a sparsely-distributed and statistically-independent representation provides a valid criterion for the self-organizing classification and integration of context signals. A biologically motivated unsupervised learning algorithm that approximate such a representation is derived from maximum likelihood. This representation is beneficial for learning in the cerebellum by simplifying the credit assignment problem between what must be learned and the relevant signals in the current context for learning it. Due to its statistical independence, this representation is also beneficial for life-long learning by reducing the destructive interference across tasks, while retaining the ability to generalize. The benefits of the learning algorithm are investigated in a spiking model that learns to generate predictive smooth pursuit eye movements to follow target trajectories.     

Determination of the surface coverage of Re/Al2O3 catalysts by ion scattering spectroscopy and low‐temperature CO adsorption

The surface coverage of a series of rhenia–alumina catalysts prepared by equilibrium adsorption was determined by low‐temperature CO adsorption. The results show a linear increase in the coverage of the Re oxide on the alumina support up to 24% with increasing Re loading from 0 to ca. 5.6 wt%. This is consistent with a uniform build‐up of the surface species. The coverage values measured by this method agreed very well with those obtained by ion scattering spectroscopy. This revised version was published online in July 2006 with corrections to the Cover Date.