Field configured assembly: Programmed manipulation and self-assembly at the mesoscale

Field configured assembly is a programmable force field method that permits rapid, "hands-free" manipulation, assembly, and integration of mesoscale objects and devices. In this method, electric fields, configured by specific addressing of receptor and counter electrode sites pre-patterned at a silicon chip substrate, drive the field assisted transport, positioning, and localization of mesoscale devices at selected receptor locations. Using this approach, we demonstrate field configured deterministic and stochastic self-assembly of model mesoscale devices, i.e., 50 mum diameter, 670 nm emitting GaAs-based light emitting diodes, at targeted receptor sites on a silicon chip. The versatility of the field configured assembly method suggests that it is applicable to self-assembly of a wide variety of functionally integrated nanoscale and mesoscale systems.

Self-focusing and envelope pulse generation in nonlinear magnetic metamaterials

The self-modulation of waves propagating in nonlinear magnetic metamaterials is investigated. Considering the propagation of a modulated amplitude magnetic field in such a medium, we show that the self-modulation of the carrier wave leads to a spontaneous energy localization via the generation of localized envelope structures (envelope solitons), whose form and properties are discussed. These results are also supported by numerical calculations.

Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.

Fermion localization on a split brane

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Studies of subcellular localization and complex formation of the Agrobacterium tumefaciens transport ATPase VirB11

The VirB11 ATPase is an essential component of an Agrobacterium tumefaciens type IV bacterial secretion system that transfers oncogenic nucleoprotein complexes to susceptible plant cells. This dissertation investigates the subcellular localization and homo-oligomeric state of the VirB11 ATPase in order to provide insights about the assembly of the protein as a subunit of this membrane-associated transfer system. Subcellular fractionation studies and quantitative immunoblot analysis demonstrated that $\sim$30% of VirB11 partitioned as soluble protein and $\sim$70% was tightly associated with the bacterial cytoplasmic membrane. No differences were detected in VirB11 subcellular localization and membrane association in the presence or absence of other transport system components. Mutations in virB11 affecting protein function were mapped near the amino terminus, just upstream of a region encoding a Walker 'A' nucleotide-binding site, and within the Walker 'A' motif partitioned almost exclusively with the cytoplasmic membrane, suggesting that an activity associated with nucleotide binding could modulate the affinity of VirB11 for the cytoplasmic membrane. Merodiploid analysis of VirB11 mutant and truncation derivatives provided strong evidence that VirB11 functions as a homo- or heteromultimer and that the C-terminal half of VirB11 contains a protein interaction domain. A combination of biochemical and molecular genetic approaches suggested that VirB11 and the green fluorescence protein (GFP) formed a mixed multimer as demonstrated by immunoprecipitation experiments with anti-GFP antibodies. Second, a hybrid protein composed of VirB11 fused to the N-terminal DNA-binding domain of bacteriophage $\lambda$ cI repressor conferred immunity to $\lambda$ superinfection, demonstrating that VirB11 self-association promotes dimerization of the chimeric repressor. A conserved Walker 'A' motif, though required for VirB11 function in T-complex export, was not necessary for VirB11 self-association. Sequences in both the N- and the C-terminal halves of the protein were found to contribute to self-association of the full length protein. Chemical cross-linking experiments with His$\sb6$ tagged VirB11 suggested that VirB11 probably assembles into a higher order homo-oligomeric complex. ^

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

Self-assembly into nanoparticles is essential for receptor mediated uptake of therapeutic antisense oligonucleotides

Antisense oligonucleotides (ASOs) have the potential of revolutionizing medicine due to their ability to manipulate gene function for therapeutic purposes. ASOs are chemically modified and/or incorporated with nanoparticles to enhance their stability and cellular uptake; however, one of the biggest challenges is the poor understanding of their uptake mechanism, which is needed for designing better ASOs with high activity and low toxicity. Here, we study the uptake mechanism of three therapeutically relevant ASOs (peptide-conjugated phosphorodiamidate morpholino (P-PMO), 2?Omethyl phosphorothioate (2?OMe) and phosphorothioated tricyclo DNA (tcDNA) that have been optimized to induce exon skipping in models of Deuchenne muscular dystrophy (DMD). We show that P-PMO and tcDNA have high propensity to spontaneously self-assemble into nanoparticles. P-PMO forms micelles of defined size and their net charge (zeta potential) is dependent on the medium and concentration. In biomimetic conditions and at low concentrations P-PMO obtains net negative charge and its uptake is mediated by class A scavenger receptor subtypes (SCARAs) as shown by competitive inhibition and RNAi silencing experiments in-vitro. In-vivo, the activity of P-PMO was significantly decreased in SCARA1 knock-out mice compared to wild-type animals. Additionally, we show that SCARA1 is involved in the uptake of tcDNA and 2?OMe as shown by competitive inhibition and co-localization experiments. Surface plasmon resonance binding analysis to SCARA1 demonstrated that P-PMO and tcDNA have higher binding profiles to the receptor compared to 2?OMe. These results demonstrate receptor-mediated uptake for a range of ASO chemistries, a mechanism that is dependent on their self-assembly into nanoparticles.

Self-positioning and mapping of rectangular rooms with sectorized narrowband antennas

A system for simultaneous 2D estimation of rectangular room and transceiver localization is proposed. The system is based on two radio transceivers, both capable of full duplex operations (simultaneous transmission and reception). This property enables measurements of channel impulse response (CIR) at the same place the signal is transmitted (generated), commonly known as self-to-self CIR. Another novelty of the proposed system is the spatial CIR discrimination that is possible with the receiver antenna design which consists of eight sectorized antennas with 45° aperture in the horizontal plane and total coverage equal to the isotropic one. The dimensions of a rectangular room are reconstructed directly from spatial radio impulse responses by extracting the information regarding round trip time (RTT). Using radar approach estimation of walls and corners positions is derived. Tests using measured data were performed, and the simulation results confirm the feasibility of the approach.

Cattle-powered nodes experience in a heterogeneous network for localization of herds

A heterogeneous network, mainly based on nodes that use harvested energy to self-energize is presented and its use demonstrated. The network, mostly kinetically powered, has been used for the localization of herds in grazing areas under extreme climate conditions. The network consists of secondary and primary nodes. The former, powered by a kinetic generator, take advantage of animal movements to broadcast a unique identifier. The latter are battery-powered and gather secondarynode transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. Because a limited human interaction is desirable, the aim of this network is to reduce the battery count of the system.

The parameterless self-organizing map algorithm

The parameterless self-organizing map (PLSOM) is a new neural network algorithm based on the self-organizing map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighborhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.

Energy-efficient self -organization of wireless acoustic sensor networks for ground target tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Nerve localization techniques for peripheral nerve block and possible future directions

Ultrasound guidance is now a standard nerve localization technique for peripheral nerve block (PNB). Ultrasonography allows simultaneous visualization of the target nerve, needle, local anesthetic injectate and surrounding anatomical structures. Accurate deposition of local anesthetic next to the nerve is essential to the success of the nerve block procedure. Unfortunately, due to limitations in the visibility of both needle tip and nerve surface, the precise relationship between needle tip and target nerve is unknown at the moment of injection. Importantly, nerve injury may result both from an inappropriately placed needle tip and inappropriately placed local anesthetic. The relationship between the block needle tip and target nerve is of paramount importance to the safe conduct of peripheral nerve block. This review summarizes the evolution of nerve localization in regional anesthesia, characterizes a problem faced by clinicians in performing ultrasound guided nerve block and explores the potential technological solutions to this problem.

Using Coding to Improve Localization and Backscatter Communication Performance in Low-Power Sensor Networks

Backscatter communication is an emerging wireless technology that recently has gained an increase in attention from both academic and industry circles. The key innovation of the technology is the ability of ultra-low power devices to utilize nearby existing radio signals to communicate. As there is no need to generate their own energetic radio signal, the devices can benefit from a simple design, are very inexpensive and are extremely energy efficient compared with traditional wireless communication. These benefits have made backscatter communication a desirable candidate for distributed wireless sensor network applications with energy constraints.

The backscatter channel presents a unique set of challenges. Unlike a conventional one-way communication (in which the information source is also the energy source), the backscatter channel experiences strong self-interference and spread Doppler clutter that mask the information-bearing (modulated) signal scattered from the device. Both of these sources of interference arise from the scattering of the transmitted signal off of objects, both stationary and moving, in the environment. Additionally, the measurement of the location of the backscatter device is negatively affected by both the clutter and the modulation of the signal return.

This work proposes a channel coding framework for the backscatter channel consisting of a bi-static transmitter/receiver pair and a quasi-cooperative transponder. It proposes to use run-length limited coding to mitigate the background self-interference and spread-Doppler clutter with only a small decrease in communication rate. The proposed method applies to both binary phase-shift keying (BPSK) and quadrature-amplitude modulation (QAM) scheme and provides an increase in rate by up to a factor of two compared with previous methods.

Additionally, this work analyzes the use of frequency modulation and bi-phase waveform coding for the transmitted (interrogating) waveform for high precision range estimation of the transponder location. Compared to previous methods, optimal lower range sidelobes are achieved. Moreover, since both the transmitted (interrogating) waveform coding and transponder communication coding result in instantaneous phase modulation of the signal, cross-interference between localization and communication tasks exists. Phase discriminating algorithm is proposed to make it possible to separate the waveform coding from the communication coding, upon reception, and achieve localization with increased signal energy by up to 3 dB compared with previous reported results.

The joint communication-localization framework also enables a low-complexity receiver design because the same radio is used both for localization and communication.

Simulations comparing the performance of different codes corroborate the theoretical results and offer possible trade-off between information rate and clutter mitigation as well as a trade-off between choice of waveform-channel coding pairs. Experimental results from a brass-board microwave system in an indoor environment are also presented and discussed.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.