Feasibility of manual teach-and-replay and continuous impedance shaping for robotic locomotor training following spinal cord injury

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients.     

Supervised learning and frequency domain averaging-based adaptive channel estimation scheme for filterbank multicarrier with offset quadrature amplitude modulation

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.     

Ochratoxin A in dried vine fruit: method development and survey

A method is described for the determination of concentrations of the mycotoxin ochratoxin A in dried vine fruits (currants, raisins and sultanas) using acidic methanolic extraction,immunoaffinity chromatography clean-up and HPLC determination. The limit of detection was estimated as 0.2mug/kg, and recoveries of 63-77% were achieved at 5mug/kg. HPLC-mass spectrometric confirmation of the identity of ochratoxin was obtained. Ochratoxin A and aflatoxins were determined in 60 samples of retail dried vine fruits purchased in the United Kingdom. Ochratoxin A was found in excess of 0.2mug/kg in 19 of 20 currant, 17 of 20 sultana and 17 of 20 raisin samples examined, an overall incidence of 88% . The maximum level found was 53.6mug/kg. No aflatoxin was found in any sample analysed, using a method with a detection limit of 0.2mug/kg for each of aflatoxin B1, B2, G1 and G2.     

Surveillance of stored grain from the 1997 harvest in the United Kingdom for ochratoxin A

This survey examined 306 samples of farm-stored wheat, barley and oats as received at, or tested by, central grain depots in the UK. Samples were taken from lorries or from stored grain using the existing inhouse procedures used for quality checking and examined for ochratoxin A using a fully validated analytical HPLC method with a detection limit of 0.1 μg/kg. Ochratoxin A was detected in 21 % of the samples examined, with barley more frequently contaminated than wheat. Mean concentrations of ochratoxin A found for all samples were 0.69 μg/kg in barley, 0.29 μg/kg in wheat and 0.15 μg/kg in oats. The highest concentration found was 17.8 μg/kg in a barley feed although concentrations of 81 and 30 μg/kg were found in 'reject-grade' wheat samples whose results were excluded from the main survey. In summary, 2.7 and 0.3 % of samples exceeded concentrations of 5 and 10 μg/kg respectively. There appeared to be significant relationships between ochratoxin A concentrations and moisture content, storage time and geographical area. Although conditions at harvest in 1997 were quite variable countrywide and often wet, results were similar to those found in earlier surveys carried out in the UK.     

A floating sleeve antenna yields localized hepatic microwave ablation

We report a novel coaxial antenna for hepatic microwave ablation. This device uses a floating sleeve, that is, a metal conductor electrically isolated from the outer connector of the antenna coaxial body, to achieve a highly localized specific absorption rate pattern that is independent of insertion depth. This floating sleeve coaxial dipole antenna has low power reflection in the 2.4-GHz IMS band. Ex vivo experiments confirm our numerical simulation results. Index Terms-Ablation, coaxial aperture antennas, finite element methods, floating sleeve, microwave heating.     

Learning algorithms for joint resource block and power allocation in underlay D2D networks

We investigate the problem of resource block (RB) and power allocation jointly and in a distributed manner using game theoretic learning solutions, in an underlay device-to-device network where device pairs communicate directly with each other by reusing the spectrum allocated to the cellular users. We formulate the joint RB and power allocation as multi-agent learning problems with discrete strategy sets; and suggest partially distributed and fully distributed learning algorithms to determine the RB and power level to be used by each device pair. The partially distributed algorithms, viz., Fictitious Play and its variant Fading Memory Joint Strategy Fictitious Play with Inertia, achieve Nash Equilibrium (NE) of the sum-rate maximization game in a static wireless environment. The completely distributed and uncoupled Stochastic Learning Algorithm converges to pure strategy NE of the interference mitigation game in a time-varying radio environment. We provide proofs for the existence of NE and convergence of the learning algorithms to the NE. Performance of the proposed schemes are evaluated in log-normal, Rayleigh and Nakagami fading environments and compared with an existing hybrid scheme and a centralized scheme. The simulation results show that the partially distributed schemes give the same performance as the centralized scheme, and the fully distributed scheme gives similar performance as the hybrid scheme but with much reduced signaling and computation overhead.     

InRout - A QoS aware route selection algorithm for industrial wireless sensor networks

Wireless sensor networks are a key enabling technology for industrial monitoring applications where the use of wireless infrastructure allows high adaptivity and low cost in terms of installation and retrofitting. To facilitate the move from the current wired designs to wireless designs, concerns regarding reliability must be satisfied. Current standardization efforts for industrial wireless systems lack specification on efficient routing protocols that mitigate reliability concerns. Consequently, this work presents the InRout route selection algorithm, where local information is shared among neighbouring nodes to enable efficient, distributed route selection while satisfying industrial application requirements and considering sensor node resource limitations. Route selection is described as a multi-armed bandit task and uses Q-learning techniques to obtain the best available solution with low overhead. A performance comparison with existing approaches demonstrates the benefits of the InRout algorithm, which satisfies typical quality of service requirements for industrial monitoring applications while considering sensor node resources. Simulation results show that InRout can provide gains ranging from 4% to 60% in the number of successfully delivered packets when compared to current approaches with much lower control overhead.     

Supported Lipid Bilayer Assembly on PEDOT:PSS Films and Transistors

Lipid bilayers are widely employed as a model system to investigate interactions between cells and their environment. Supported lipid bilayers (SLB) with integrated transmembrane proteins are emerging as a preferred platform for sensing applications. Challenges lie in the generation of SLB on surfaces which allow transduction of signals for characterization of lipid bilayer and incorporated transmembrane proteins. For the first time, the formation of SLBs is shown on films of the conducting polymer, poly(3,4‐ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS), using traditional methods for characterizing lipid bilayer quality and function (QCM‐D, FRAP) combined with impedance spectroscopy. Further, partial formation of SLBs on PEDOT:PSS based organic electrochemical transistors (OECTs) is successfully demonstrated, as well as the ability to integrate and sense the ion pore α‐hemolysin, confirming the sensitivity of the OECT as a transducer of biological membrane function. This work represents a highly promising first step toward the use of such OECTs for functional readout of transmembrane proteins in their native environment.