Tunable Circularly Polarized Terahertz Radiation from Magnetized Gas Plasma

It is shown, by simulation and theory, that circularly or elliptically polarized terahertz radiation can be generated when a static magnetic (B) field is imposed on a gas target along the propagation direction of a two-color laser driver. The radiation frequency is determined by $\sqrt{\omega_p^2+{\omega_c^2}/{4}} + {\omega_c}/{2}$, where $\omega_p$ is the plasma frequency and $\omega_c$ is the electron cyclotron frequency. With the increase of the B field, the radiation changes from a single-cycle broadband waveform to a continuous narrow-band emission. In high-B-field cases, the radiation strength is proportional to $\omega_p^2/\omega_c$. The B field provides a tunability in the radiation frequency, spectrum width, and field strength.Comment: 6 pages, 5 figure

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.Comment: 7 page

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page

Laser opacity in underdense preplasma of solid targets due to quantum electrodynamics effects

We investigate how next-generation laser pulses at 10 PW $-$ 200 PW interact with a solid target in the presence of a relativistically underdense preplasma produced by amplified spontaneous emission (ASE). Laser hole boring and relativistic transparency are strongly restrained due to the generation of electron-positron pairs and $\gamma$-ray photons via quantum electrodynamics (QED) processes. A pair plasma with a density above the initial preplasma density is formed, counteracting the electron-free channel produced by the hole boring. This pair-dominated plasma can block the laser transport and trigger an avalanche-like QED cascade, efficiently transfering the laser energy to photons. This renders a 1-$\rm\mu m$-scalelength, underdense preplasma completely opaque to laser pulses at this power level. The QED-induced opacity therefore sets much higher contrast requirements for such pulse in solid-target experiments than expected by classical plasma physics. Our simulations show for example, that proton acceleration from the rear of a solid with a preplasma would be strongly impaired.Comment: 5 figure

A new genetic algorithm approach to smooth path planning for mobile robots

Purpose-The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve. Design/methodology/approach-The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. Findings-This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed. Research limitations/implications-There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research. Originality/value-The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.This work was supported in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China and the Higher Educational Science and Technology Program of Shandong Province of China under Grant J14LN34

Progress and perspective of interface design in garnet electrolyte-based all-solid-state batteries

Inorganic solid-state electrolytes (SSEs) are nonflammable alternatives to the commercial liquid-phase electrolytes. This enables the use of lithium (Li) metal as an anode, providing high-energy density and improved stability by avoiding unwanted liquid-phase chemical reactions. Among the different types of SSEs, the garnet-type electrolytes witness a rapid development and are considered as one of the top candidates to pair with Li metal due to their high ionic conductivity, thermal, and electrochemical stability. However, the large resistances at the interface between garnet-type electrolytes and cathode/anode are the major bottlenecks for delivering desirable electrochemical performances of all-solid-state batteries (SSBs). The electrolyte/anode interface also suffers from metallic dendrite formation, leading to rapid performance degradation. This is a fundamental material challenge due to the poor contact and wettability between garnet-type electrolytes with electrode materials. Here, we summarize and analyze the recent contributions in mitigating such materials challenges at the interface. Strategies used to address these challenges are divided into different categories with regard to their working principles. On one hand, progress has been made in the anode/garnet interface, such as the successful application of Li-alloy anode and different artificial interlayers, significantly improving interfacial performance. On the other hand, the desired cathode/garnet interface is still hard to reach due to the complex chemical and physical structure at the cathode. The common methods used are nanostructured cathode host and sintering additives for increasing the contact area. On the basis of this information, we present our views on the remaining challenges and future research of electrode/garnet interface. This review not only motivates the need for further understanding of the fundamentals, stability, and modifications of the garnet/electrode interfaces but also provides guidelines for the future design of the interface for SSB

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed