Locally minimum-variance filtering of 2-D systems over sensor networks with measurement degradations: A distributed recursive algorithm

This article tackles the recursive filtering problem for an array of 2-D systems over sensor networks with a given topology. Both the measurement degradations of the network outputs and the stochastic perturbations of network couplings are modeled to reflect engineering practice by introducing some random variables with given statistics. The goal of the addressed problem is to devise the distributed recursive filters capable of cooperatively estimating the true state in order to ensure locally minimal upper bound (UB) on the second-order moment of the filtering error (also viewed as the general error variance). For this purpose, the general error variance regarding the underlying target plant is first provided to facilitate the subsequent filter design, and then a certain UB on the error variance is constructed by exploiting the stochastic analysis and the induction approach. Furthermore, in view of the inherent sparsity of the sensor network, the gain parameters of the desired distributed filters are determined, and the proposed recursive filtering algorithm is shown to be scalable. Finally, an illustrative example is given to demonstrate the validity of the established filtering strategy

Distributed filtering for complex networks under multiple event-triggered transmissions within node-wise communications

This paper focuses on the distributed filtering and fault estimation problems for a class of complex networks, where the communications between filters at different nodes are subject to dynamic event-triggered (DET) transmissions. A filter is constructed at each node by resorting to local measurements and information from neighboring nodes and thus the developed algorithm can be carried out distributedly. Different from the clock-driven signal transmissions in traditional distributed filtering schemes, the transmissions of both state estimates and the upper bounds of filtering error covariances (FECs) between the nodes are monitored by a multiple DET strategy to reduce unnecessary burdens in the links. Under DET transmissions, an upper bound of the FEC is obtained and then minimized via parameterizing the filter recursively. Novel sufficient conditions, which are dependent on locally available information, are provided to guarantee the uniform boundedness of the FEC at each node. The proposed method is used to solve the fault estimation problem in complex networks, where the estimation error is ensured to be exponentially bounded. Some illustrative examples are employed to show the effectiveness of our algorithm

Joint state and fault estimation of complex networks under measurement saturations and stochastic nonlinearities

In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously. In the presence of measurement saturations and stochastic nonlinearities, upper bounds of the error covariances of the fault estimates are recursively obtained and then minimized. Sufficient conditions are proposed to guarantee the existence and the unbiasedness of the developed estimator. Our developed estimator design algorithm is distributed because it depends only on the local information and the information from the neighboring subsystems, thereby avoiding the usage of a center estimator. Finally, simulation results are presented to show the performance of the proposed strategy in simultaneously estimating the states and faults

A novel algorithm for quantized particle filtering with multiple degrading sensors: degradation estimation and target tracking

This paper addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements

Auxiliary particle filtering over sensor networks under protocols of amplify-and-forward and decode-and-forward relays

In this paper, the particle filtering problem is investigated for a class of stochastic systems with multiple sensors under signal relays. To improve the performance of signal transmissions, a relay is deployed between each sensor and the remote filter. Both amplify-and-forward (AF) and decode-and-forward (DF) relays are considered under certain transmission protocols. Stochastic series are employed to describe multiplicative channel gains and additive transmission noises. Novel likelihood functions are derived based on the AF/DF relay models under different protocols. With the measurements collected from all the sensor nodes, a new centralized auxiliary particle filter (APF) is designed by resorting to the statistical information of the channel gains and transmission noises. Next, a consensus-based distributed APF is further established at each node that requires only locally available information. Finally, the effectiveness of the proposed filtering approach is demonstrated through target tracking simulation examples in different situations. </p

DNA Detection Using Plasmonic Enhanced Near-Infrared Photoluminescence of Gallium Arsenide

Efficient near-infrared detection of specific DNA with single nucleotide polymorphism selectivity is important for diagnostics and biomedical research. Herein, we report the use of gallium arsenide (GaAs) as a sensing platform for probing DNA immobilization and targeting DNA hybridization, resulting in ∼8-fold enhanced GaAs photoluminescence (PL) at ∼875 nm. The new signal amplification strategy, further coupled with the plasmonic effect of Au nanoparticles, is capable of detecting DNA molecules with a detection limit of 0.8 pM and selectivity against single base mismatches. Such an ultrasensitive near-infrared sensor can find a wide range of biochemical and biomedical applications

DNA-Directed Assembly of Asymmetric Nanoclusters Using Janus Nanoparticles

Asymmetric assembly of nanomaterials has attracted broad interests because of their unique anisotropic properties that are different from those based on the more widely reported symmetric assemblies. Despite the potential advantages, programmable fabrication of asymmetric structure in nanoscale remains a challenge. We report here a DNA-directed approach for the assembly of asymmetric nanoclusters using Janus nanoparticles as building blocks. DNA-functionalized spherical gold nanoparticles (AuNSs) can be selectively attached onto two different hemispheres of DNA-functionalized Janus nanoparticle (JNP) through DNA hybridization. Complementary and invasive DNA strands have been used to control the degree and reversibility of the assembly process through programmable base-pairing interactions, resulting in a series of modular and asymmetric nanostructures that allow systematic study of the size-dependent assembly process. We have also shown that the attachment of the AuNSs onto the gold surface of the Janus nanoparticle results in red shifting of the UV–vis and plasmon resonance spectra

Mechanistic Insight into DNA-Guided Control of Nanoparticle Morphologies

Although shapes and surface characteristics of nanoparticles are known to play important roles in defining their properties, it remains challenging to fine-tune the morphologies systematically and predictably. Recently, we have shown that DNA molecules can serve as programmable ligands to fine-tune the morphologies of nanomaterials. Despite this discovery, the mechanism of how the morphology can be controlled and the roles of the DNA molecules in contributing to such control are not understood. We herein report mechanistic investigation of DNA-mediated morphological evolution of gold nanoprism seeds into nonagon, hexagon, and six-pointed stars, some of which display rough surfaces, in the presence of homo-oligomeric T30, G20, C30, and A30. The growth, elucidated through various analytical methods including UV–vis, SEM, TEM, zeta potential, fluorescence, and cyclic voltammetry, is found to occur in two stages: control of shape, followed by control of thickness. A careful analysis of diffraction patterns of the nanoprism seeds as well as the resulting intermediate shapes by TEM allowed us to deduce the exact sequence of shape evolution. Through systematic comparison of the nanoparticle growth process, the DNA molecules were found to play important roles by influencing diffusion of the Au precursor to the seed and modulating the growth through differences in DNA desorption, density, and mobility on the seed surface. These insights into the mechanism of DNA-guided control of nanomaterial morphologies provide deeper understanding of the interactions between the DNA and nanomaterials and will allow better control of the shapes and surface properties of many nanomaterials

pH-Dependent Evolution of Five-Star Gold Nanostructures: An Experimental and Computational Study

Dendritic structures, such as snowflakes, have been observed in nature in far-from-equilibrium growth conditions. Mimicking these structures at the nanometer scale can result in nanomaterials with interesting properties for applications, such as plasmonics and biosensors. However, reliable production and systematic fine-tuning morphologies of these nanostructures, with novel hierarchical or complex structures, along with theoretical understanding of these processes, are still major challenges in the field. Here, we report a new method of using pH to control HAuCl₄ reduction by hydroxylamine for facile production of gold nanostructures with morphologies in various symmetries and hierarchies, both in solution and on solid surface. Of particular interest is the observation of five-star-like dendritic and hierarchical gold nanostructures under certain reaction conditions. Phase-field modeling was used to understand the growth and formation dynamics of the five-star and other gold complex nanostructures, and the results not only explained the experimental observations, but also predicted control of the nanostructural morphologies using both pH and hydroxylamine concentrations. In addition to revealing interesting growth dynamics in forming fascinating complex gold nanostructures, the present work provides a pH-directed morphology control method as a facile way to synthesize and fine-tune the morphology of hierarchical gold nanostructures