Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.

Strain engineering of selective chemical adsorption on monolayer MoS2

Nanomaterials are prone to influence by chemical adsorption because of their large surface to volume ratios. This enables sensitive detection of adsorbed chemical species which, in turn, can tune the property of the host material. Recent studies discovered that single and multi-layer molybdenum disulfide (MoS2) films are ultra-sensitive to several important environmental molecules. Here we report new findings from ab inito calculations that reveal substantially enhanced adsorption of NO and NH3 on strained monolayer MoS2 with significant impact on the properties of the adsorbates and the MoS2 layer. The magnetic moment of adsorbed NO can be tuned between 0 and 1 μB; strain also induces an electronic phase transition between half-metal and metal. Adsorption of NH3 weakens the MoS2 layer considerably, which explains the large discrepancy between the experimentally measured strength and breaking strain of MoS2 films and previous theoretical predictions. On the other hand, adsorption of NO2, CO, and CO2 is insensitive to the strain condition in the MoS2 layer. This contrasting behavior allows sensitive strain engineering of selective chemical adsorption on MoS2 with effective tuning of mechanical, electronic, and magnetic properties. These results suggest new design strategies for constructing MoS2-based ultrahigh-sensitivity nanoscale sensors and electromechanical devices.

Control, operation and power sharing among parallel converter-interfaced DERs in a microgrid in the presence of unbalanced and harmonic loads

This paper demonstrates power management and control of DERs in an autonomous MG. The paper focuses on the control and performance of converter-interfaced DERs in voltage controlled mode. Several case studies are considered for a MG based on the different types of loads supplied by the MG (i.e. balanced three-phase, unbalanced, single-phase and harmonic loads). DERs are controlled by adjusting the voltage magnitude and angle in their converter output through droop control, in a decentralized concept. Based on this control method, DERs can successfully share the total demand of the MG in the presence of any type of loads. This includes proper total power sharing, unbalanced power sharing as well as harmonic power sharing, depending on the load types. The efficacy of the proposed power control, sharing and management among DERs in a microgrid is validated through extensive simulation studies using PSCAD/EMTDC.

Distribution harmonic state estimation based on a modified PSO considering parameters uncertainty

This paper presents a new algorithm based on a Modified Particle Swarm Optimization (MPSO) to estimate the harmonic state variables in a distribution networks. The proposed algorithm performs the estimation for both amplitude and phase of each injection harmonic currents by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as the uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WTs). The main features of the proposed MPSO algorithm are usage of a primary and secondary PSO loop and applying the mutation function. The simulation results on 34-bus IEEE radial and a 70-bus realistic radial test networks are presented. The results demonstrate that the speed and the accuracy of the proposed Distribution Harmonic State Estimation (DHSE) algorithm are very excellent compared to the algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO, and Honey Bees Mating Optimization (HBMO).

A novel algorithm based on honey bee mating optimization for distribution harmonic state estimation including distributed generators

This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).

Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

Efficient selective identity-based encryption without random oracles

We construct two efficient Identity-Based Encryption (IBE) systems that admit selective-identity security reductions without random oracles in groups equipped with a bilinear map. Selective-identity secure IBE is a slightly weaker security model than the standard security model for IBE. In this model the adversary must commit ahead of time to the identity that it intends to attack, whereas in an adaptive-identity attack the adversary is allowed to choose this identity adaptively. Our first system—BB1—is based on the well studied decisional bilinear Diffie–Hellman assumption, and extends naturally to systems with hierarchical identities, or HIBE. Our second system—BB2—is based on a stronger assumption which we call the Bilinear Diffie–Hellman Inversion assumption and provides another approach to building IBE systems. Our first system, BB1, is very versatile and well suited for practical applications: the basic hierarchical construction can be efficiently secured against chosen-ciphertext attacks, and further extended to support efficient non-interactive threshold decryption, among others, all without using random oracles. Both systems, BB1 and BB2, can be modified generically to provide “full” IBE security (i.e., against adaptive-identity attacks), either using random oracles, or in the standard model at the expense of a non-polynomial but easy-to-compensate security reduction.

Tethering for Selective Synthesis of 2,2′-Biphenols : the Acetal Method

2,2'-Biphenols are a large and diverse group of compounds with exceptional properties both as ligands and bioactive agents. Traditional methods for their synthesis by oxidative dimerisation are often problematic and lead to mixtures of ortho- and para-connected regioisomers. To compound these issues, an intermolecular dimerisation strategy is often inappropriate for the synthesis of heterodimers. The ‘acetal method’ provides a solution for these problems: stepwise tethering of two monomeric phenols enables heterodimer synthesis, enforces ortho regioselectivity and allows relatively facile and selective intramolecular reactions to take place. The resulting dibenzo[1,3]dioxepines have been analysed by quantum chemical calculations to obtain information about the activation barrier for ring flip between the enantiomers. Hydrolytic removal of the dioxepine acetal unit revealed the 2,2′-biphenol target.

An MMP13-selective inhibitor delays primary tumor growth and the onset of tumor-associated osteolytic lesions in experimental models of breast cancer

We investigated the effects of the matrix metalloproteinase 13 (MMP13)-selective inhibitor, 5-(4-{4-[4-(4-fluorophenyl)-1,3-oxazol-2-yl]phenoxy}phenoxy)-5-(2-methoxyethyl) pyrimidine-2,4,6(1H,3H,5H)-trione (Cmpd-1), on the primary tumor growth and breast cancer-associated bone remodeling using xenograft and syngeneic mouse models. We used human breast cancer MDA-MB-231 cells inoculated into the mammary fat pad and left ventricle of BALB/c Nu/Nu mice, respectively, and spontaneously metastasizing 4T1.2-Luc mouse mammary cells inoculated into mammary fat pad of BALB/c mice. In a prevention setting, treatment with Cmpd-1 markedly delayed the growth of primary tumors in both models, and reduced the onset and severity of osteolytic lesions in the MDA-MB-231 intracardiac model. Intervention treatment with Cmpd-1 on established MDA-MB-231 primary tumors also significantly inhibited subsequent growth. In contrast, no effects of Cmpd-1 were observed on soft organ metastatic burden following intracardiac or mammary fat pad inoculations of MDA-MB-231 and 4T1.2-Luc cells respectively. MMP13 immunostaining of clinical primary breast tumors and experimental mice tumors revealed intra-tumoral and stromal expression in most tumors, and vasculature expression in all. MMP13 was also detected in osteoblasts in clinical samples of breast-to-bone metastases. The data suggest that MMP13-selective inhibitors, which lack musculoskeletal side effects, may have therapeutic potential both in primary breast cancer and cancer-induced bone osteolysis.

Selective involvement of TIMP-2 in the second activational cleavage of pro-MMP-2 : refinement of the pro-MMP-2 activation mechanism

A tissue inhibitor of metalloproteinases-2 (TIMP-2)-independent mechanism for generating the first activational cleavage of pro-matrix metalloproteinase-2 (MMP-2) was identified in membrane type-1 MMP (MT1-MMP)-transfected MCF-7 cells and confirmed in TIMP-2-deficient fibroblasts. In contrast, the second MMP-2-activational step was found to be TIMP-2 dependent in both systems. MMP-2 hemopexin C-terminal domain was found to be critical for the first step processing, confirming a need for membrane tethering. We propose that the intermediate species of MMP-2 forms the well-established trimolecular complex (MT1-MMP/TIMP-2/MMP-2) for further TIMP-2-dependent autocatalytic cleavage to the fully active species. This alternate mechanism may supplement the traditional TIMP-2-mediated first step mechanism.

Second-harmonic generation from biological tissues : effect of excitation wavelength

We report on the measurement of second-harmonic signals from hyperplastic parenchyma and stroma in malignant human prostate tissue under femtosecond pulsed illumination in the wavelength range from 730 to 870 nm. In particular, the relationship of the second-harmonic generation to the excitation wavelength is measured. The result in these two regions behaves considerably differently and thus provides a possible indicator for identifying tissue components and malignancy.

Dependence of second-harmonic signals generated in malignant human prostate tissue on excitation wavelength

The dependence of second harmonic generation (SHG) from hyperplastic parenchyma and stroma in maligant human prostate tissue on excitation wavelengths was measured. A femtosecond pulsed laser, a scanning microscope and a spectrograph were used to perform the measurements. The spectra were measured under excitation power of 10 mW at excitation wavelengths of 730 nm, 750 nm, 800 nm, 850 nm and 890 nm. Analysis suggested that the SHG in prostate tissue is highly structured and wavelength dependent signifying its ability to be used as an indicator for recognizing tissue components, ultrastructures, micro-environments and diseases.

Selective neuronal differentiation of neural stem cells induced by nanosecond microplasma agitation

An essential step for therapeutic and research applications of stem cells is their ability to differentiate into specific cell types. Neuronal cells are of great interest for medical treatment of neurodegenerative diseases and traumatic injuries of central nervous system (CNS), but efforts to produce these cells have been met with only modest success. In an attempt of finding new approaches, atmospheric-pressure room-temperature microplasma jets (MPJs) are shown to effectively direct in vitro differentiation of neural stem cells (NSCs) predominantly into neuronal lineage. Murine neural stem cells (C17.2-NSCs) treated with MPJs exhibit rapid proliferation and differentiation with longer neurites and cell bodies eventually forming neuronal networks. MPJs regulate ~. 75% of NSCs to differentiate into neurons, which is a higher efficiency compared to common protein- and growth factors-based differentiation. NSCs exposure to quantized and transient (~. 150. ns) micro-plasma bullets up-regulates expression of different cell lineage markers as β-Tubulin III (for neurons) and O4 (for oligodendrocytes), while the expression of GFAP (for astrocytes) remains unchanged, as evidenced by quantitative PCR, immunofluorescence microscopy and Western Blot assay. It is shown that the plasma-increased nitric oxide (NO) production is a factor in the fate choice and differentiation of NSCs followed by axonal growth. The differentiated NSC cells matured and produced mostly cholinergic and motor neuronal progeny. It is also demonstrated that exposure of primary rat NSCs to the microplasma leads to quite similar differentiation effects. This suggests that the observed effect may potentially be generic and applicable to other types of neural progenitor cells. The application of this new in vitro strategy to selectively differentiate NSCs into neurons represents a step towards reproducible and efficient production of the desired NSC derivatives. © 2013.

Size- and orientation-selective Si nanowire growth : thermokinetic effects of naeoscale plasma chemistry

A multiscale, multiphase thermokinetic model is used to show the effective control of the growth orientation of thin Si NWs for nanoelectronic devices enabled by nanoscale plasma chemistry. It is shown that very thin Si NWs with [110] growth direction can nucleate at much lower process temperatures and pressures compared to thermal chemical vapor deposition where [111]-directed Si NWs are predominantly grown. These findings explain a host of experimental results and offer the possibility of energy- and matter-efficient, size- and orientation-controlled growth of [110] Si NWs for next-generation nanodevices.

Nanoscale plasma chemistry enables fast, size-selective nanotube nucleation

The possibility of fast, narrow-size/chirality nucleation of thin single-walled carbon nanotubes (SWCNTs) at low, device-tolerant process temperatures in a plasma-enhanced chemical vapor deposition (CVD) is demonstrated using multiphase, multiscale numerical experiments. These effects are due to the unique nanoscale reactive plasma chemistry (NRPC) on the surfaces and within Au catalyst nanoparticles. The computed three-dimensional process parameter maps link the nanotube incubation times and the relative differences between the incubation times of SWCNTs of different sizes/chiralities to the main plasma- and precursor gas-specific parameters and explain recent experimental observations. It is shown that the unique NRPC leads not only to much faster nucleation of thin nanotubes at much lower process temperatures, but also to better selectivity between the incubation times of SWCNTs with different sizes and chiralities, compared to thermal CVD. These results are used to propose a time-programmed kinetic approach based on fast-responding plasmas which control the size-selective, narrow-chirality nucleation and growth of thin SWCNTs. This approach is generic and can be used for other nanostructure and materials systems.