Showing papers by "Stevens Institute of Technology published in 2017"

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Abstract: Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Abstract: Deep Learning has recently become hugely popular in machine learning, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level DP applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage

Abstract: Remote data integrity checking (RDIC) enables a data storage server, say a cloud server, to prove to a verifier that it is actually storing a data owner’s data honestly. To date, a number of RDIC protocols have been proposed in the literature, but most of the constructions suffer from the issue of a complex key management, that is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key-homomorphic cryptographic primitive to reduce the system complexity and the cost for establishing and managing the public key authentication framework in PKI-based RDIC schemes. We formalize ID-based RDIC and its security model, including security against a malicious cloud server and zero knowledge privacy against a third party verifier. The proposed ID-based RDIC protocol leaks no information of the stored data to the verifier during the RDIC process. The new construction is proven secure against the malicious server in the generic group model and achieves zero knowledge privacy against a verifier. Extensive security analysis and implementation results demonstrate that the proposed protocol is provably secure and practical in the real-world applications.

Approaching the intrinsic photoluminescence linewidth in transition metal dichalcogenide monolayers

Abstract: Excitonic states in monolayer transition metal dichalcogenides (TMDCs) have been the subject of extensive recent interest. Their intrinsic properties can, however, be obscured due to the influence of inhomogeneity in the external environment. Here we report methods for fabricating high quality TMDC monolayers with narrow photoluminescence (PL) linewidth approaching the intrinsic limit. We find that encapsulation in hexagonal boron nitride (h-BN) sharply reduces the PL linewidth, and that passivation of the oxide substrate by an alkyl monolayer further decreases the linewidth and also minimizes the charged exciton (trion) peak. The combination of these sample preparation methods results in much reduced spatial variation in the PL emission, with a full-width-at-half-maximum as low as 1.7 meV. Analysis of the PL line shape yields a homogeneous width of 1.43 ± 0.08 meV and inhomogeneous broadening of 1.1 ± 0.3 meV.

Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression

Abstract: People can benefit from disclosing negative emotions or stigmatized facets of their identities, and psychologists have noted that imagery can be an effective medium for expressing difficult emotions. Social network sites like Instagram offer unprecedented opportunity for image-based sharing. In this paper, we investigate sensitive self-disclosures on Instagram and the responses they attract. We use visual and textual qualitative content analysis and statistical methods to analyze self-disclosures, associated comments, and relationships between them. We find that people use Instagram to engage in social exchange and story-telling about difficult experiences. We find considerable evidence of social support, a sense of community, and little aggression or support for harmful or pro-disease behaviors. Finally, we report on factors that influence engagement and the type of comments these disclosures attract. Personal narratives, food and beverage, references to illness, and self-appearance concerns are more likely to attract positive social support. Posts seeking support attract significantly more comments. CAUTION: This paper includes some detailed examples of content about eating disorders and self-injury illnesses.

Redactable Blockchain – or – Rewriting History in Bitcoin and Friends

Abstract: We put forward a new framework that makes it possible to re-write or compress the content of any number of blocks in decentralized services exploiting the blockchain technology. As we argue, there are several reasons to prefer an editable blockchain, spanning from the necessity to remove inappropriate content and the possibility to support applications requiring re-writable storage, to "the right to be forgotten." Our approach generically leverages so-called chameleon hash functions (Krawczyk and Rabin, NDSS '00), which allow determining hash collisions efficiently, given a secret trapdoor information. We detail how to integrate a chameleon hash function in virtually any blockchain-based technology, for both cases where the power of redacting the blockchain content is in the hands of a single trusted entity and where such a capability is distributed among several distrustful parties (as is the case with Bitcoin). We also report on a proof-of-concept implementation of a redactable blockchain, building on top of Nakamoto's Bitcoin core. The prototype only requires minimal changes to the way current client software interprets the information stored in the blockchain and to the current blockchain, block, or transaction structures. Moreover, our experiments show that the overhead imposed by a redactable blockchain is small compared to the case of an immutable one.

Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT

Abstract: User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep learning based user authentication scheme to accurately identify each individual user. Extensive experiments in two typical indoor environments, a university office and an apartment, are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% and 91% authentication accuracy with 11 subjects through walking and stationary activities, respectively.

Learned Contextual Feature Reweighting for Image Geo-Localization

Abstract: We address the problem of large scale image geo-localization where the location of an image is estimated by identifying geo-tagged reference images depicting the same place. We propose a novel model for learning image representations that integrates context-aware feature reweighting in order to effectively focus on regions that positively contribute to geo-localization. In particular, we introduce a Contextual Reweighting Network (CRN) that predicts the importance of each region in the feature map based on the image context. Our model is learned end-to-end for the image geo-localization task, and requires no annotation other than image geo-tags for training. In experimental results, the proposed approach significantly outperforms the previous state-of-the-art on the standard geo-localization benchmark datasets. We also demonstrate that our CRN discovers task-relevant contexts without any additional supervision.

Approaching the Intrinsic Photoluminescence Linewidth in Transition Metal Dichalcogenide Monolayers

Abstract: Excitonic states in monolayer transition metal dichalcogenides (TMDCs) have been the subject of extensive recent interest. Their intrinsic properties can, however, be obscured due to the influence of inhomogeneity in the external environment. Here we report methods for fabricating high quality TMDC monolayers with narrow photoluminescence (PL) linewidth approaching the intrinsic limit. We find that encapsulation in hexagonal boron nitride (h-BN) sharply reduces the PL linewidth, and that passivation of the oxide substrate by an alkyl monolayer further decreases the linewidth and also minimizes the charged exciton (trion) peak. The combination of these sample preparation methods results in much reduced spatial variation in the PL emission, with a full-width-at-half-maximum as low as 1.7 meV. Analysis of the PL line shape yields a homogeneous width of 1.43$\pm$0.08 meV and inhomogeneous broadening of 1.1$\pm$0.3 meV.

Nanoengineered Superhydrophobic Surfaces of Aluminum with Extremely Low Bacterial Adhesivity

Abstract: Bacterial adhesion and biofilm formation on surfaces are troublesome in many industrial processes. Here, nanoporous and nanopillared aluminum surfaces were engineered by anodizing and postetching processes and made hydrophilic (using the inherent oxide layer) or hydrophobic (applying a Teflon coating) with the aim of discouraging bacterial adhesion. Adhesion of Staphylococcus aureus ATCC 12600 (Gram-positive, spherically shaped) and Escherichia coli K-12 (Gram-negative, rod-shaped) was evaluated to the nanoengineered surfaces under both static and flow conditions (fluid shear rate of 37 s–1). Compared to a nonstructured electropolished flat surface, the nanostructured surfaces significantly reduced the number of adhering colony forming units (CFUs) for both species, as measured using agar plating. For the hydrophilic surfaces, this was attributed to a decreased contact area, reducing bacterial adhesion forces on nanoporous and nanopillared surfaces to 4 and 2 nN, respectively, from 8 nN on flat surfaces. ...

Enhancing the measurement of information technology (IT) business alignment and its influence on company performance

Abstract: Studies for over 30 years have consistently indicated that enterprise-level Business-Information Technology (IT) alignment is a pervasive problem. While significant progress has been made to understand alignment, research on IT alignment is still plagued by several problems. First, most alignment models approach alignment as a static relationship in contrast to analyzing the scope and variance of activities through which the alignment is (or can be) attained. Second, most alignment models are not founded on strong theoretical foundations. Third, because of their static view, these models do not guide how organizations can improve alignment. This study addresses these weaknesses using a capability-based lens. It formulates and operationalizes a formative construct rooted in the theory of dynamic capabilities and defines the scope and nature of activities that contribute to alignment. The construct identifies six dimensions promoting alignment: (1) IT-Business Communications; (2) Use of Value Analytics; (3) Approaches to Collaborative Governance; (4) Nature of the affiliation/partnership; (5) Scope of IT initiatives; and (6) Development of IT Skills. The construct measures are validated in terms of their dimensionality, item pool sampling, and the nomological and predictive validity. The research uses Partial Least Squares (PLS) to statistically validate the construct using a dataset covering over 3000 global participants including nearly 400 Fortune 1000 companies. All construct dimensions contribute significantly to the level of alignment and the construct shows strong nomological and predictive validity by demonstrating a statistically significant impact on firm performance. Scholars can leverage this research to explore additional activity-based constructs of IT-business alignment.

Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems

Abstract: We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the MS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramer-Rao bound (CRB) results for channel parameters and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs and present a clear advantage over the compressed sensing-based method.

On self-selection biases in online product reviews

Abstract: Online product reviews help consumers infer product quality, and the mean (average) rating is often used as a proxy for product quality. However, two self-selection biases, acquisition bias (mostly consumers with a favorable predisposition acquire a product and hence write a product review) and underreporting bias (consumers with extreme, either positive or negative, ratings are more likely to write reviews than consumers with moderate product ratings), render the mean rating a biased estimator of product quality, and they result in the well-known J-shaped (positively skewed, asymmetric, bimodal) distribution of online product reviews. To better understand the nature and consequences of these two self-selection biases, we analytically model and empirically investigate how these two biases originate from consumers' purchasing and reviewing decisions, how these decisions shape the distribution of online product reviews over time, and how they affect the firm's product pricing strategy. Our empirical results reveal that consumers do realize both self-selection biases and attempt to correct for them by using other distributional parameters of online reviews, besides the mean rating. However, consumers cannot fully account for these two self-selection biases because of bounded rationality. We also find that firms can strategically respond to these self-selection biases by adjusting their prices. Still, since consumers cannot fully correct for these two self-selection biases, product demand, the firm's profit, and consumer surplus may all suffer from the two self-selection biases. This paper has implications for consumers to leverage online product reviews to infer true product quality, for commercial websites to improve the design of their online product review systems, and for product manufacturers to predict the success of their products.

Modulation classification using convolutional Neural Network based deep learning model

Abstract: Deep learning (DL) is a powerful classification technique that has great success in many application domains However, its usage in communication systems has not been well explored In this paper, we address the issue of using DL in communication systems, especially for modulation classification Convolutional neural network (CNN) is utilized to complete the classification task We convert the raw modulated signals into images that have a grid-like topology and feed them to CNN for network training Two existing approaches, including cumulant and support vector machine (SVM) based classification algorithms, are involved for performance comparison Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection

Nonmagnetic Quantum Emitters in Boron Nitride with Ultranarrow and Sideband-Free Emission Spectra

Abstract: Hexagonal boron nitride (hBN) is an emerging material in nanophotonics and an attractive host for color centers for quantum photonic devices. Here, we show that optical emission from individual quantum emitters in hBN is spatially correlated with structural defects and can display ultranarrow zero-phonon line width down to 45 μeV if spectral diffusion is effectively eliminated by proper surface passivation. We demonstrate that undesired emission into phonon sidebands is largely absent for this type of emitter. In addition, magneto-optical characterization reveals cycling optical transitions with an upper bound for the g-factor of 0.2 ± 0.2. Spin-polarized density functional theory calculations predict possible commensurate transitions between like-spin electron states, which are in excellent agreement with the experimental nonmagnetic defect center emission. Our results constitute a step toward the realization of narrowband quantum light sources and the development of spin–photon interfaces within 2D mate...

An adaptive portfolio trading system

Abstract: A reinforcement learning trading algorithm with expected drawdown risk is proposed.The expected maximum drawdown is shown to improve portfolio signal generation.The effectiveness of the method is validated using different transaction costs.An adaptive portfolio rebalancing system with automated retraining is recommended. Dynamic control theory has long been used in solving optimal asset allocation problems, and a number of trading decision systems based on reinforcement learning methods have been applied in asset allocation and portfolio rebalancing. In this paper, we extend the existing work in recurrent reinforcement learning (RRL) and build an optimal variable weight portfolio allocation under a coherent downside risk measure, the expected maximum drawdown, E(MDD). In particular, we propose a recurrent reinforcement learning method, with a coherent risk adjusted performance objective function, the Calmar ratio, to obtain both buy and sell signals and asset allocation weights. Using a portfolio consisting of the most frequently traded exchange-traded funds, we show that the expected maximum drawdown risk based objective function yields superior return performance compared to previously proposed RRL objective functions (i.e. the Sharpe ratio and the Sterling ratio), and that variable weight RRL long/short portfolios outperform equal weight RRL long/short portfolios under different transaction cost scenarios. We further propose an adaptive E(MDD) risk based RRL portfolio rebalancing decision system with a transaction cost and market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system responds to transaction cost effects better and outperforms hedge fund benchmarks consistently.

Removal Mechanisms of Phosphate by Lanthanum Hydroxide Nanorods: Investigations using EXAFS, ATR-FTIR, DFT, and Surface Complexation Modeling Approaches.

Abstract: Lanthanum-based materials are effective for sequestering phosphate in water, however, their removal mechanisms remain unclear, and the effects of environmentally relevant factors have not yet been studied. Hereby, this study explored the mechanisms of phosphate removal using La(OH)3 by employing extended X-ray absorption spectroscopy (EXAFS), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), density functional theory (DFT) and chemical equilibrium modeling. The results showed that surface complexation was the primary mechanism for phosphate removal and in binary phosphate configurations, namely diprotonated bidentate mononuclear (BM-H2) and bidentate binuclear (BB-H2), coexisting on La(OH)3 in acidic conditions. By increasing the pH to 7, BM-H1 and BB-H2 were the two major configurations governing phosphate adsorption on La(OH)3, whereas BB-H1 was the dominant configuration of phosphate adsorption at pH 9. With increasing phosphate loading, the phosphate configuration of on ...

Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

Abstract: Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e., Weaving , Swerving , Sideslipping , Fast U-turn , Turning with a wide radius , and Sudden braking . Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormal D riving behavior D etection and i D entification system, $D^{3}$ , to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods, Support Vector Machine (SVM) and Neuron Networks (NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that $D^{3}$ achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.

Semisupervised Feature Selection Based on Relevance and Redundancy Criteria

Abstract: Feature selection aims to gain relevant features for improved classification performance and remove redundant features for reduced computational cost. How to balance these two factors is a problem especially when the categorical labels are costly to obtain. In this paper, we address this problem using semisupervised learning method and propose a max-relevance and min-redundancy criterion based on Pearson’s correlation (RRPC) coefficient. This new method uses the incremental search technique to select optimal feature subsets. The new selected features have strong relevance to the labels in supervised manner, and avoid redundancy to the selected feature subsets under unsupervised constraints. Comparative studies are performed on binary data and multicategory data from benchmark data sets. The results show that the RRPC can achieve a good balance between relevance and redundancy in semisupervised feature selection. We also compare the RRPC with classic supervised feature selection criteria (such as mRMR and Fisher score), unsupervised feature selection criteria (such as Laplacian score), and semisupervised feature selection criteria (such as sSelect and locality sensitive). Experimental results demonstrate the effectiveness of our method.

Dark matter constraints from a joint analysis of dwarf Spheroidal galaxy observations with VERITAS

Abstract: We present constraints on the annihilation cross section of weakly interacting massive particles dark matter based on the joint statistical analysis of four dwarf galaxies with VERITAS. These results are derived from an optimized photon weighting statistical technique that improves on standard imaging atmospheric Cherenkov telescope (IACT) analyses by utilizing the spectral and spatial properties of individual photon events. We report on the results of ∼230 hours of observations of five dwarf galaxies and the joint statistical analysis of four of the dwarf galaxies. We find no evidence of gamma-ray emission from any individual dwarf nor in the joint analysis. The derived upper limit on the dark matter annihilation cross section from the joint analysis is 1.35×10-23 cm3 s-1 at 1 TeV for the bottom quark (bb) final state, 2.85×10-24 cm3 s-1 at 1 TeV for the tau lepton (τ+τ-) final state and 1.32×10-25 cm3 s-1 at 1 TeV for the gauge boson (γγ) final state.

Why copper is preferred over iron for oxygen activation and reduction in haem-copper oxidases

Abstract: Haem-copper oxidase (HCO) catalyses the natural reduction of oxygen to water using a haem-copper centre. Despite decades of research on HCOs, the role of non-haem metal and the reason for nature's choice of copper over other metals such as iron remains unclear. Here, we use a biosynthetic model of HCO in myoglobin that selectively binds different non-haem metals to demonstrate 30-fold and 11-fold enhancements in the oxidase activity of Cu- and Fe-bound HCO mimics, respectively, as compared with Zn-bound mimics. Detailed electrochemical, kinetic and vibrational spectroscopic studies, in tandem with theoretical density functional theory calculations, demonstrate that the non-haem metal not only donates electrons to oxygen but also activates it for efficient O-O bond cleavage. Furthermore, the higher redox potential of copper and the enhanced weakening of the O-O bond from the higher electron density in the d orbital of copper are central to its higher oxidase activity over iron. This work resolves a long-standing question in bioenergetics, and renders a chemical-biological basis for the design of future oxygen-reduction catalysts.

Moving Target Detection Using Colocated MIMO Radar on Multiple Distributed Moving Platforms

Abstract: In this paper, we consider the problem of moving target detection, and a novel radar system with multiple moving platforms is proposed. Each moving platform is equipped with multiple colocated antennas and serves as a transmitter or a receiver. Thus, this system possesses the advantages of both distributed and colocated multiple-input multiple-output radars. To exploit the clutter sparsity in the surveillance area, a novel compressed sensing (CS)-based model is proposed. Since the clutter cannot exactly reside on discretized grids often employed by most CS approaches, a novel two-step algorithm which extends the orthogonal matching pursuit is proposed to reconstruct the off-grid clutter. Then, a fusion center combines all received signals by using a generalized likelihood ratio test to detect the moving target. To further improve the detection performance, a novel online waveform optimization algorithm is developed to maximize the signal-to-clutter-and-noise ratios of each transmitter platform. Extensive simulation results are provided to demonstrate the effectiveness of the proposed radar system and algorithms.

Oil-Impregnated Nanoporous Oxide Layer for Corrosion Protection with Self-Healing

Abstract: The major drawback of current passivation techniques for preventing corrosion is the lack of ability to withstand any external damages or local defects. In this study, oil-impregnated nanoporous anodic aluminum oxide (AAO) layers are investigated to overcome such limitations and thus advance corrosion protection. By completely filling hydrophobized nanopores with oil via a solvent exchange method, a highly water-repellent surface that prevents the penetration of corrosive media into the AAO layer and hence the corrosion of aluminum is achieved. The impregnation of oil into the hydrophobic nanoporous AAO layer enhances the corrosion resistance of an AAO layer by two and four orders of magnitude compared to that of a hydrophobic (i.e., air-entrained) and a bare (hydrophilic) AAO, respectively. In the presence of local defects, the oil impregnated within the hydrophobic nanoporous AAO layer naturally permeates into the defects and ultimately inhibits the exposure of the aluminum surface to corrosive media. Whereas the corrosion current density of the air-entrained hydrophobic AAO layer increases by more than 30 times after cracks, that of the oil-impregnated AAO layer increases by no more than 4 times, showing superior anticorrosion property even after there are cracks, owing to the effective self-healing capability.

Miniaturized Circularly Polarized Square Slot Antenna With Enhanced Axial-Ratio Bandwidth Using an Antipodal Y-strip

Abstract: A novel single-fed, wideband, circularly polarized slot antenna is proposed and fabricated. Wideband circular polarization is obtained by introducing an antipodal Y-strip to a square slot antenna. The feedline is a U-shaped microstrip line that provides a wide impedance bandwidth. The overall size of the antenna is only 28 × 28 mm 2 (0.3 λ o × 0.3 λ o ). A prototype of the antenna is fabricated and tested. The measured bandwidths for the axial ratio (AR <; 3 dB) and relative impedance (|S 11 | <; -10 dB) are 41.3% (from 4.4 to 6.67 GHz) and 84% (from 3.25 to 8 GHz), respectively, and the antenna has a stable radiation pattern and a gain of greater than 3 dBi over the entire circular polarization frequency band.

Purcell-enhanced quantum yield from carbon nanotube excitons coupled to plasmonic nanocavities.

Abstract: Single-walled carbon nanotubes (SWCNTs) are promising absorbers and emitters to enable novel photonic applications and devices but are also known to suffer from low optical quantum yields. Here we demonstrate SWCNT excitons coupled to plasmonic nanocavity arrays reaching deeply into the Purcell regime with Purcell factors (F P) up to F P = 180 (average F P = 57), Purcell-enhanced quantum yields of 62% (average 42%), and a photon emission rate of 15 MHz into the first lens. The cavity coupling is quasi-deterministic since the photophysical properties of every SWCNT are enhanced by at least one order of magnitude. Furthermore, the measured ultra-narrow exciton linewidth (18 μeV) reaches the radiative lifetime limit, which is promising towards generation of transform-limited single photons. To demonstrate utility beyond quantum light sources we show that nanocavity-coupled SWCNTs perform as single-molecule thermometers detecting plasmonically induced heat at cryogenic temperatures in a unique interplay of excitons, phonons, and plasmons at the nanoscale.