Showing papers by "University of Electro-Communications published in 2020"
TL;DR: In this paper, the spin degree of freedom of electrons and/or holes, which can also interact with their orbital moments, is described with respect to the spin generation methods as detailed in Sections 2-~-9.
614 citations
TL;DR: In this article, the A site cation of tin halide perovskite solar cells was replaced with formamidinium cation to achieve a tolerance factor of nearly 1.
292 citations
TL;DR: The significance and technical challenges of applying FL in vehicular IoT, and future research directions are discussed, and a brief survey of existing studies on FL and its use in wireless IoT is conducted.
Abstract: Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then, we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.
194 citations
TL;DR: A comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area and helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in M EC.
Abstract: Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.
186 citations
TL;DR: A self-sacrificed templating approach is developed to obtain edge-enriched FeN4 sites integrated in the highly graphitic nanosheet architecture, which demonstrates unprecedented catalytic activity and stability for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER)
Abstract: Single-atom FeN4 sites at the edges of carbon substrates are considered more active for oxygen electrocatalysis than those in plane; however, the conventional high-temperature pyrolysis process does not allow for precisely engineering the location of the active site down to atomic level. Enlightened by theoretical prediction, herein, a self-sacrificed templating approach is developed to obtain edge-enriched FeN4 sites integrated in the highly graphitic nanosheet architecture. The in situ formed Fe clusters are intentionally introduced to catalyze the growth of graphitic carbon, induce porous structure formation, and most importantly, facilitate the preferential anchoring of FeN4 to its close approximation. Due to these attributes, the as-resulted catalyst (denoted as Fe/N-G-SAC) demonstrates unprecedented catalytic activity and stability for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) by showing an impressive half-wave potential of 0.89 V for the ORR and a small overpotential of 370 mV at 10 mA cm-2 for the OER. Moreover, the Fe/N-G-SAC cathode displays encouraging performance in a rechargeable Zn-air battery prototype with a low charge-discharge voltage gap of 0.78 V and long-term cyclability for over 240 cycles, outperforming the noble metal benchmarks.
183 citations
TL;DR: In this article, metal complexes are used for converting CO2 into chemical fuels with a photocatalyst and sunlight, which is an appealing approach to address climate deterioration and energy crisis.
Abstract: Converting CO2 into chemical fuels with a photocatalyst and sunlight is an appealing approach to address climate deterioration and energy crisis. Metal complexes are superb candidates for CO2 reduc...
175 citations
TL;DR: A double protecting strategy that ensures robust acidic water splitting on Ir SAEs by dispersing Ir atoms in/onto Fe nanoparticles and embedding IrFe nanoparticles into nitrogen-doped carbon nanotubes (Ir-SA@Fe@NCNT) is conceived and realized.
Abstract: Single-atom electrocatalysts (SAEs) can realize the target of low-cost by maximum atomic efficiency. However, they usually suffer performance decay due to high energy states, especially in a harsh ...
154 citations
TL;DR: A proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair is proposed.
Abstract: In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.
123 citations
TL;DR: It is found that aging of SnCl2 precursor solution for electron transporting layer can promote the Voc of CsPbI2Br solar cells employing a dopant-free-polymer HTM over 1.40 V and efficiency over 15.5% with high reproducibility.
Abstract: CsPbI2Br perovskite solar cells have attracted much attention because of the rapid development in their efficiency and their great potential as a top cell of tandem solar cells. However, the VOC outputs observed so far in most cases are far from that desired for a top cell. Up to now, with various kinds of treatments, the reported champion VOC is only 1.32 V, with a VOC deficit of 0.60 V. In this work, we found that aging of the SnCl2 precursor solution for the electron-transporting layer can promote the VOC of CsPbI2Br solar cells by employing a dopant-free-polymer hole transport material (HTM) over 1.40 V and efficiency over 15.5% with high reproducibility. With the champion VOC of 1.43 V, the VOC deficit was reduced to <0.50 V, which is achieved for the first time. This simple technique of SnCl2 solution aging forms a uniform and smooth amorphous SnOx film with pure Sn4+, elevates the conduction band of SnOx, and reduces the interfacial gaps and the trap state density of the device, resulting in enhancement in average VOC from ∼1.2 V in the nonaged case to ∼1.4 V in the aged case. Furthermore, the device using an aged SnCl2 solution also exhibits a much better long-term stability than that made of the fresh solution. These achievements in dopant/additive-free CsPbI2Br solar cells can be useful for future research on CsPbI2Br and tandem solar cells.
104 citations
TL;DR: A collaborative learning-based routing scheme for multi-access vehicular edge computing environment that employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead and is preemptively changed based on the learned information.
Abstract: Some Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the “proactive” and “preemptive” approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.
104 citations
TL;DR: A general strategy for the design and preparation of highly active SACs for electrochemical energy devices with beneficial microstructure and abundant highly active FeN 5 moieties resulting from the secondary doping is offered.
Abstract: Single-atom catalysts (SACs) with nitrogen-coordinated nonprecious metal sites have exhibited inimitable advantages in electrocatalysis. However, a large room for improving their activity and durability remains. Herein, we construct atomically dispersed Fe sites in N-doped carbon supports by secondary-atom-doped strategy. Upon the secondary doping, the density and coordination environment of active sites can be efficiently tuned, enabling the simultaneous improvement in the number and reactivity of the active site. Besides, structure optimizations in terms of the enlarged surface area and improved hydrophilicity can be achieved simultaneously. Due to the beneficial microstructure and abundant highly active FeN5 moieties resulting from the secondary doping, the resultant catalyst exhibits an admirable half-wave potential of 0.81 V versus 0.83 V for Pt/C and much better stability than Pt/C in acidic media. This work would offer a general strategy for the design and preparation of highly active SACs for electrochemical energy devices.
TL;DR: Sub-second scintillations of aurorae are precisely controlled by fine-scale chirping rhythms in chorus, demonstrating that resonant interaction between energetic electrons and chorus waves in magnetospheres orchestrates the complex behavior of aurora on Earth and other magnetized planets.
Abstract: The brightness of aurorae in Earth’s polar region often beats with periods ranging from sub-second to a few tens of a second. Past observations showed that the beat of the aurora is composed of a superposition of two independent periodicities that co-exist hierarchically. However, the origin of such multiple time-scale beats in aurora remains poorly understood due to a lack of measurements with sufficiently high temporal resolution. By coordinating experiments using ultrafast auroral imagers deployed in the Arctic with the newly-launched magnetospheric satellite Arase, we succeeded in identifying an excellent agreement between the beats in aurorae and intensity modulations of natural electromagnetic waves in space called “chorus”. In particular, sub-second scintillations of aurorae are precisely controlled by fine-scale chirping rhythms in chorus. The observation of this striking correlation demonstrates that resonant interaction between energetic electrons and chorus waves in magnetospheres orchestrates the complex behavior of aurora on Earth and other magnetized planets.
TL;DR: It is shown that highly efficient pRTP materials allow for high-resolution gated emission with a size of the diffraction limit using small-scale and low-cost photodetectors.
Abstract: Persistent (lifetime > 100 ms) room-temperature phosphorescence (pRTP) is important for state-of-the-art security and bioimaging applications An unclear relationship between chromophores and physical parameters relating to pRTP has prevented obtaining an RTP yield of over 50% and a lifetime over 1 s Here highly efficient pRTP is reported under ambient conditions from heavy atom-free chromophores A heavy atom-free aromatic core substituted with a long-conjugated amino group considerably accelerates the phosphorescence rate independent of the intramolecular vibration-based nonradiative rate from the lowest excited triplet state One of the designed heavy atom-free dopant chromophores presents an RTP yield of 50% with a lifetime of 1 s under ambient conditions The afterglow brightness under strong excitation is at least 104 times stronger than that of conventional long-persistent luminescence emitters Here it is shown that highly efficient pRTP materials allow for high-resolution gated emission with a size of the diffraction limit using small-scale and low-cost photodetectors
Boston University1, University of New Hampshire2, Tohoku University3, Nagoya University4, University of Calgary5, University Centre in Svalbard6, University of Electro-Communications7, Goddard Space Flight Center8, University of Maryland, College Park9, National Institute of Polar Research10, University of Southampton11, University of Oulu12
TL;DR: In this article, the authors review fundamental properties and recent advances of diffuse and pulsating aurora and discuss open questions about the relationship between high-energy precipitation and wave-particle interaction.
Abstract: This chapter reviews fundamental properties and recent advances of diffuse and pulsating aurora. Diffuse and pulsating aurora often occurs on closed field lines and involves energetic electron precipitation by wave-particle interaction. After summarizing the definition, large-scale morphology, types of pulsation, and driving processes, we review observation techniques, occurrence, duration, altitude, evolution, small-scale structures, fast modulation, relation to high-energy precipitation, the role of ECH waves, reflected and secondary electrons, ionosphere dynamics, and simulation of wave-particle interaction. Finally we discuss open questions of diffuse and pulsating aurora.
Posted Content•
TL;DR: A new class of signal injection attacks on microphones by physically converting light to sound is proposed, showing how an attacker can inject arbitrary audio signals to a target microphone by aiming an amplitude-modulated light at the microphone's aperture.
Abstract: We propose a new class of signal injection attacks on microphones by physically converting light to sound. We show how an attacker can inject arbitrary audio signals to a target microphone by aiming an amplitude-modulated light at the microphone's aperture. We then proceed to show how this effect leads to a remote voice-command injection attack on voice-controllable systems. Examining various products that use Amazon's Alexa, Apple's Siri, Facebook's Portal, and Google Assistant, we show how to use light to obtain control over these devices at distances up to 110 meters and from two separate buildings. Next, we show that user authentication on these devices is often lacking, allowing the attacker to use light-injected voice commands to unlock the target's smartlock-protected front doors, open garage doors, shop on e-commerce websites at the target's expense, or even unlock and start various vehicles connected to the target's Google account (e.g., Tesla and Ford). Finally, we conclude with possible software and hardware defenses against our attacks.
Nagoya University1, National Institute of Information and Communications Technology2, Kyoto University3, Japan Aerospace Exploration Agency4, University of Electro-Communications5, Tohoku University6, Graduate University for Advanced Studies7, National Institute of Polar Research8, University of Oulu9, Goddard Space Flight Center10, University of Iowa11, The Aerospace Corporation12
TL;DR: In this article, the wave-particle interactions were simulated by simulating the wave particle interactions and it was shown that subrelativistic electron microbursts formed the high-energy tail of pulsating aurora (PsA).
Abstract: In this study, by simulating the wave-particle interactions, we show that sub-relativistic/relativistic electron microbursts form the high-energy tail of pulsating aurora (PsA). Whistler-mode choru...
TL;DR: The recent research including photoexcited multiple exciton generation (MEG), hot electron extraction, and carrier transfer between adjacent QDs, as well as carrier injection and recombination at each interface of QDSCs are discussed in detail herein.
Abstract: The certified power conversion efficiency (PCE) record of colloidal quantum dot solar cells (QDSCs) has considerably improved from below 4% to 16.6% in the last few years. However, the record PCE value of QDSCs is still substantially lower than the theoretical efficiency. So far, there have been several reviews on recent and significant achievements in QDSCs, but reviews on photoexcited carrier dynamics in QDSCs are scarce. The photovoltaic performances of QDSCs are still limited by the photovoltage, photocurrent and fill factor that are mainly determined by the photoexcited carrier dynamics, including carrier (or exciton) generation, carrier extraction or transfer, and the carrier recombination process, in the devices. In this review, the photoexcited carrier dynamics in the whole QDSCs, originating from individual quantum dots (QDs) to the entire device as well as the characterization methods used for analyzing the photoexcited carrier dynamics are summarized and discussed. The recent research including photoexcited multiple exciton generation (MEG), hot electron extraction, and carrier transfer between adjacent QDs, as well as carrier injection and recombination at each interface of QDSCs are discussed in detail herein. The influence of photoexcited carrier dynamics on the physiochemical properties of QDs and photovoltaic performances of QDSC devices is also discussed.
TL;DR: A cooperative spectrum sensing network is designed and established to realize wide-area broadband spectrum sensing and obtain big spectrum data, and a novel dual-end machine learning model is proposed to improve the precision and real-time prediction of heterogeneous spectrum states.
Abstract: Although spectrum sensing is commonly used in modern wireless communications to determine spectrum resources, the rapid development of wireless communications has generated massive heterogeneous spectrum data, which has dramatically increased the complexity of spectrum sensing. Machine-learning-assisted spectrum sensing, as an emerging and promising technique, provides an effective way to find available spectrum resources through the analysis of big spectrum data. In this article, a bigdata- based intelligent spectrum sensing method is proposed to improve heterogeneous spectrum sensing. Specifically, a cooperative spectrum sensing network is designed and established to realize wide-area broadband spectrum sensing and obtain big spectrum data. The effectiveness of such a network has been verified through detection probability simulation. To improve the reliability of spectrum sensing data, the correlations of the big spectrum data in time domain, frequency domain and space domain have been investigated, and the spectrum similarity has been obtained. Then a novel dual-end machine learning model is proposed to improve the precision and real-time prediction of heterogeneous spectrum states. Furthermore, a big spectrum data clustering mechanism is adopted to facilitate data matching and heterogeneous spectrum prediction. Finally, the comprehensive spectrum state is obtained through heterogeneous spectrum data fusion.
Graduate University for Advanced Studies1, University of Tokyo2, Laboratoire d'Annecy-le-Vieux de physique des particules3, University of Electro-Communications4, California Institute of Technology5, National Tsing Hua University6, Leibniz University of Hanover7, University of Paris8, University of Amsterdam9, University of Padua10
TL;DR: The first demonstration of a frequency-dependent squeezed vacuum source able to reduce quantum noise of advanced gravitational-wave detectors in their whole observation bandwidth is reported.
Abstract: The astrophysical reach of current and future ground-based gravitational-wave detectors is mostly limited by quantum noise, induced by vacuum fluctuations entering the detector output port. The replacement of this ordinary vacuum field with a squeezed vacuum field has proven to be an effective strategy to mitigate such quantum noise and it is currently used in advanced detectors. However, current squeezing cannot improve the noise across the whole spectrum because of the Heisenberg uncertainty principle: when shot noise at high frequencies is reduced, radiation pressure at low frequencies is increased. A broadband quantum noise reduction is possible by using a more complex squeezing source, obtained by reflecting the squeezed vacuum off a Fabry-Perot cavity, known as filter cavity. Here we report the first demonstration of a frequency-dependent squeezed vacuum source able to reduce quantum noise of advanced gravitational-wave detectors in their whole observation bandwidth. The experiment uses a suspended 300-m-long filter cavity, similar to the one planned for KAGRA, Advanced Virgo, and Advanced LIGO, and capable of inducing a rotation of the squeezing ellipse below 100 Hz.
TL;DR: This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement.
Abstract: Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC.
20 May 2020
TL;DR: In this article, a direct all-solid-state Z-scheme heterojunction photocatalyst of a g-C3N4-coated tree-like α-Fe2O3 was rationally constructed toward CO2 conversion.
Abstract: A direct all-solid-state Z-scheme heterojunction photocatalyst of a g-C3N4-coated tree-like α-Fe2O3 was rationally constructed toward CO2 conversion. The so-called artificial tree shows excellent p...
University College Dublin1, University of Arizona2, Beijing Institute of Technology3, University of Electro-Communications4, University of Alicante5, Bulgarian Academy of Sciences6, National Chiao Tung University7, Kobe University8, Kyoto Institute of Technology9, Military University of Technology in Warsaw10, Saint Petersburg State University11, Brno University of Technology12, National Research Council13, University of California, Davis14, Columbia University15, Donostia International Physics Center16, INAF17, Bremen Institute for Applied Beam Technology18, Maynooth University19, Chinese Academy of Sciences20, Skolkovo Institute of Science and Technology21, Wakayama University22, McMaster University23
TL;DR: John T Sheridan, Raymond K Kostuk2, Antonio Fimia Gil, Y Wang4, W Lu4, H Zhong4, Y Tomita5, C Neipp6, J Francés6, S Gallego6, I Pascual6, V Marinova7,8, S-H Lin7, K-Y Hsu7, F Bruder9, S Hansen9, C Manecke9, R Meisenheimer
Abstract: This work was supported by Ministerio de Economia, Industria y Competitividad (Spain) under projects FIS2017-82919-R (MINECO/AEI/FEDER, UE) and FIS2015-66570-P (MINECO/FEDER), and by Generalitat Valenciana (Spain) under project PROMETEO II/2015/015.
TL;DR: The multiple vital sign-based screening achieved higher sensitivity than fever- based screening and represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases.
Abstract: Background: In the last two decades, infrared thermography (IRT) has been applied in quarantine stations for the screening of patients with suspected infectious disease. However, the fever-based screening procedure employing IRT suffers from low sensitivity, because monitoring body temperature alone is insufficient for detecting infected patients. To overcome the drawbacks of fever-based screening, this study aims to develop and evaluate a multiple vital sign (i.e., body temperature, heart rate and respiration rate) measurement system using RGB-thermal image sensors. Methods: The RGB camera measures blood volume pulse (BVP) through variations in the light absorption from human facial areas. IRT is used to estimate the respiration rate by measuring the change in temperature near the nostrils or mouth accompanying respiration. To enable a stable and reliable system, the following image and signal processing methods were proposed and implemented: (1) an RGB-thermal image fusion approach to achieve highly reliable facial region-of-interest tracking, (2) a heart rate estimation method including a tapered window for reducing noise caused by the face tracker, reconstruction of a BVP signal with three RGB channels to optimize a linear function, thereby improving the signal-to-noise ratio and multiple signal classification (MUSIC) algorithm for estimating the pseudo-spectrum from limited time-domain BVP signals within 15 s and (3) a respiration rate estimation method implementing nasal or oral breathing signal selection based on signal quality index for stable measurement and MUSIC algorithm for rapid measurement. We tested the system on 22 healthy subjects and 28 patients with seasonal influenza, using the support vector machine (SVM) classification method. Results: The body temperature, heart rate and respiration rate measured in a non-contact manner were highly similarity to those measured via contact-type reference devices (i.e., thermometer, ECG and respiration belt), with Pearson correlation coefficients of 0.71, 0.87 and 0.87, respectively. Moreover, the optimized SVM model with three vital signs yielded sensitivity and specificity values of 85.7% and 90.1%, respectively. Conclusion: For contactless vital sign measurement, the system achieved a performance similar to that of the reference devices. The multiple vital sign-based screening achieved higher sensitivity than fever-based screening. Thus, this system represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases.
TL;DR: In this article, a theoretical analysis of the Pb-free perovskite solar cells with an inverted p-i-n planar structure was performed using device simulation.
TL;DR: Jiang et al. as discussed by the authors incorporated the contextual information of the image patch and proposed a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL).
Abstract: Face hallucination is a technique that reconstructs high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of the human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of the image patch. In addition, when they are confronted with misalignment or the small sample size (SSS) problem, the hallucination performance is very poor. To this end, this paper incorporates the contextual information of the image patch and proposes a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL). Under the context-patch-based framework, we advance a thresholding-based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulate the case that the HR version of the input LR face is present in the training set, it thus iteratively enhances the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. In addition, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL .
TL;DR: This paper conducts a literature review on the existing research efforts of the blockchain for vehicular IoT by discussing the research problems and technical issues and point out some future research issues considering the characteristics of both blockchain and vehicular Internet of Things.
Abstract: The vehicular Internet of Things (IoT) comprises enabling technologies for a large number of important applications including collaborative autonomous driving and advanced transportation systems. Due to the mobility of vehicles, strict application requirements, and limited communication resources, the conventional centralized control fails to provide sufficient quality of service for connected vehicles, so a decentralized approach is required in the vicinity to satisfy the requirements of delay-sensitive and mission-critical applications. A decentralized system is also more resistant to the single point of failure problem and malicious attacks. Blockchain technology has been attracting great interest due to its capability of achieving a decentralized, transparent, and tamper-resistant system. There are many studies focusing on the use of blockchain in managing data and transactions in vehicular environments. However, the application of blockchain in vehicular environments also faces some technical challenges. In this paper, we first explain the fundamentals of blockchain and vehicular IoT. Then, we conduct a literature review on the existing research efforts of the blockchain for vehicular IoT by discussing the research problems and technical issues. After that, we point out some future research issues considering the characteristics of both blockchain and vehicular IoT.
TL;DR: A review of the current understanding of auroral features that appear poleward of the main auroral oval within the polar cap, especially those that are known as Sun-aligned arcs, transpolar arcs, or theta auroras, can be found in this paper.
Abstract: This paper reviews our current understanding of auroral features that appear poleward of the main auroral oval within the polar cap, especially those that are known as Sun-aligned arcs, transpolar arcs, or theta auroras. They tend to appear predominantly during periods of quiet geomagnetic activity or northwards directed interplanetary magnetic field (IMF). We also introduce polar rain aurora which has been considered as a phenomenon on open field lines. We describe the morphology of such auroras, their development and dynamics in response to solar wind-magnetosphere coupling processes, and the models that have been developed to explain them.
TL;DR: The improved film formation upon GA modification induced by the strong interaction between GA and Pb2+ delivers a champion power conversion efficiency (PCE) as high as 21.32% and large perovskite grains obtained by TGA modification but with obvious pinholes lead to an increased defect density accompanied by a decline in PCE.
Abstract: A strategy for efficaciously regulating perovskite crystallinity is proposed by using a volatile solid glycolic acid (HOCH2COOH, GA) in an FA0.85MA0.15PbI3 (FA: HC(NH2)2; MA: CH3NH3) perovskite precursor solution that is different from the common additive approach. Accompanied with the first dimethyl sulfoxide sublimation process, the subsequent sublimation of GA before 150 °C in the FA0.85MA0.15PbI3 perovskite film can artfully regulate the perovskite crystallinity without any residual after annealing. The improved film formation upon GA modification induced by the strong interaction between GA and Pb2+ delivers a champion power conversion efficiency (PCE) as high as 21.32%. In order to investigate the role of volatility in perovskite solar cells (PSCs), nonvolatile thioglycolic acid (HSCH2COOH, TGA) with a similar structure to GA is utilized as an additive reference. Large perovskite grains are obtained by TGA modification but with obvious pinholes, which directly leads to an increased defect density accompanied by a decline in PCE. Encouragingly, the champion PCE achieved for GA-based PSC device (21.32%) is almost 13% or 20% higher than those of the control device or TGA-based device. In addition, GA-modified PSCs exhibit the best stability in light-, thermal-, and humidity-based tests due to the improved film formation.
TL;DR: This work for the first time exhibited the potential of lead halide perovskite NCs for tumor diagnosis, and can highly anticipate the further in vivo biomedical applications that light up live cells.
Abstract: Great photoelectric properties can herald the high potentials of CsPbBr3 nanocrystals (NCs) to function as the fluorescent probes for early tumor diagnosis. However, the intrinsic water vulnerability of CsPbBr3 NCs highly restricts their biomedical applications. To conquer this challenge, we herein introduce a nature inspired "stress-response" method to tightly encapsulate CsPbBr3 into SiO2 nano-shells that can dramatically improve the water stability of CsPbBr3@SiO2 nanoparticles for over 48 h. We further highlighted the advantageous features of CsPbBr3@SiO2 by using them as the fluorescent probes for CT26 tumor cell imaging with their high water stability, biocompatibility, and low cytotoxicity. Our work for the first time exhibited the potential of lead halide perovskite NCs for tumor diagnosis, and can highly anticipate the further in vivo biomedical applications that light up live cells.