Human-robot synchrony: flexible assistance using adaptive oscillators

We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion-extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.     

Rotation moment invariants for recognition of symmetric objects.

In this paper, a new set of moment invariants with respect to rotation, translation, and scaling suitable for recognition of objects having N-fold rotation symmetry are presented. Moment invariants described earlier cannot be used for this purpose because most moments of symmetric objects vanish. The invariants proposed here are based on complex moments. Their independence and completeness are proven theoretically and their performance is demonstrated by experiments.     

Ester-functionalized poly(3-alkylthiophene) copolymers: Synthesis,physicochemical characterization and performance in bulk heterojunction organic solar cells

The introduction of functional moieties in the donor polymer (side chains) offers a potential pathway toward selective modification of the nanomorphology of conjugated polymer:fullerene active layer blends applied in bulk heterojunction organic photovoltaics, pursuing morphology control and solar cell stability. For this purpose, two types of poly(3-alkylthiophene) random copolymers, incorporating different amounts (10/30/50%) of ester-functionalized side chains, were efficiently synthesized using the Rieke method. The solar cell performance of the functionalized copolymers was evaluated and compared to the pristine P3HT:PCBM system. It was observed that the physicochemical and opto-electronic characteristics of the polythiophene donor material can be modified to a certain extent via copolymerization without (too much) jeopardizing the OPV efficiency, as far as the functionalized side chains are introduced in a moderate ratio (<30%) and that preference is given to side chains with a small molar volume. A range of complementary techniques – UV–Vis spectroscopy, (modulated temperature) differential scanning calorimetry, transmission electron microscopy and X-ray diffraction analysis – indicated that variations in polymer crystallinity, while maintaining a high level of regioregularity, are probably the main factor responsible for the observed differences.     

An augmented reality system for patient-specific guidance of cardiac catheter ablation procedures

We present a system to assist in the treatment of cardiac arrhythmias by catheter ablation. A patient-specific three-dimensional (3-D) anatomical model, constructed from magnetic resonance images, is merged with fluoroscopic images in an augmented reality environment that enables the transfer of electrocardiography (ECG) measurements and cardiac activation times onto the model. Accurate mapping is realized through the combination of: a new calibration technique, adapted to catheter guided treatments; a visual matching registration technique, allowing the electrophysiologist to align the model with contrast-enhanced images; and the use of virtual catheters, which enable the annotation of multiple ECG measurements on the model. These annotations can be visualized by color coding on the patient model. We provide an accuracy analysis of each of these components independently. Based on simulation and experiments, we determined a segmentation error of 0.6 mm, a calibration error in the order of 1 mm and a target registration error of 1.04 +/- 0.45 mm. The system provides a 3-D visualization of the cardiac activation pattern which may facilitate and improve diagnosis and treatment of the arrhytmia. Because of its low cost and similar advantages we believe our approach can compete with existing commercial solutions, which rely on dedicated hardware and costly catheters. We provide qualitative results of the first clinical use of the system in 11 ablation procedures.     

20.1%‐efficient crystalline silicon solar cell with amorphous silicon rear‐surface passivation

We have developed a crystalline silicon solar cell with amorphous silicon (a‐Si:H) rear‐surface passivation based on a simple process. The a‐Si:H layer is deposited at 225°C by plasma‐enhanced chemical vapor deposition. An aluminum grid is evaporated onto the a‐Si:H‐passivated rear. The base contacts are formed by COSIMA (contact formation to a‐Si:H passivated wafers by means of annealing) when subsequently depositing the front silicon nitride layer at 325°C. The a‐Si:H underneath the aluminum fingers dissolves completely within the aluminum and an ohmic contact to the base is formed. This contacting scheme results in a very low contact resistance of 3.5 ±0.2 mΩ cm² on low‐resistivity (0.5 Ω cm) p‐type silicon, which is below that obtained for conventional Al/Si contacts. We achieve an independently confirmed energy conversion efficiency of 20.1% under one‐sun standard testing conditions for a 4 cm² large cell. Measurements of the internal quantum efficiency show an improved rear surface passivation compared with reference cells with a silicon nitride rear passivation. Copyright © 2005 John Wiley & Sons, Ltd.     

Pluggable SDN framework for managing heterogeneous SDN networks

Software‐defined networking (SDN) is a new network paradigm that is separating the data plane and the control plane of the network, making one or more centralized controllers to supervise the behaviour of the entire network. Different types of SDN controller software exist, and research dealing with the difficulties of consistently integrating these different controller types has mostly been declared future work. In this paper, the Domino framework is proposed, a pluggable SDN framework for managing heterogeneous SDN networks. In contrast to related work, the proposed framework allows research into SDN networks controlled by different types of SDN controllers attempting to standardize the northbound API of them. Domino implements a microservice plugin architecture where users can link different SDN networks to a processing algorithm. Such an algorithm allows for, eg, adapting the flows by building a pipeline using plugins that either invoke other SDN operations or generic data processing algorithms. The Domino framework is evaluated by implementing a proof‐of‐concept implementation, which is tested on the initial requirements. It achieves the modifiability and the interoperability with an average successful exchange ratio of 99.99%. The performance requirements are met for the frequently used commands with an average response time of 0.26 seconds, and the framework can handle at least 72 plugins simultaneously depending on the available amount of RAM. The proposed framework is evaluated by means of the implementation of a shortest path routing algorithm between heterogeneous SDN networks.     

Pattern Noise in the Frequency ΔΣ Modulator

In this paper, we show that the first-order frequency delta–sigma modulator is equivalent to a traditional delta–sigma modulator with respect to pattern noise. We further propose two techniques for reducing the effect of pattern noise. The first technique is based on time-domain dithering which is implemented by adding white phase noise to the FM signal. The second technique is based on locating the narrow dynamic range that may be in this kind of modulators, in a pattern noise valley. This technique can utilize pattern noise to make the modulator provide significantly higher digital resolution than the white quantization noise model predicts. Finally, the theory is verified by measurements.     

Prediction of Speech Quality Based on Resilient Backpropagation Artificial Neural Network

The paper presents a system for monitoring and assessment the speech quality in the IP telephony infrastructures using modular probes. The probes are placed at key nodes in the network where aggregating packet loss data. The system dynamically measures speech quality and results are collected on a central server. For data analysis we applied four-state Markov model for modeling the impact of network impairments on speech quality, afterwards, the resilient back propagation (Rprop) algorithm was used to train a neural network. Information about the speech quality are displayed in the form of automatically generated graphs and tables. The proposed solution has been tested with selected codecs and further generalizes the already presented concepts of the speech quality estimation in the IP environment.

     

In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head

In vivo measurements of equivalent resistivities of skull (rho(skull)) and brain (rho(brain)) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that rho(skull)/rho(brain) is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in rho(brain)(average = 301 omega x cm, SD = 13%) and rho(skull)(average = 12230 omega x cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of rho(skull)/rho(brain) corresponding to different subjects. Earlier results show the necessity of calibrating rho(brain) and rho(skull) by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.