Breaking the Blockage for Big Data Transmission: Gigabit Road Communication in Autonomous Vehicles

Recently, the spectrum band beyond 60 GHz has attracted attention with the growth of traffic demand. Previous studies assumed that these bands are not suitable for vehicle communications due to the short range and high rate of blockage. However, it also means that there is no existing service or regulation designed for these bands, which makes this area free to apply. Therefore, in this article, we draw a potential map of THz vehicle transmission for autonomous vehicles to break the blockage of short-range and unstable links. First, we give a brief overview of possible waveforms followed by the specific channel at 0.1-1 THz. Then we propose an autonomous relay algorithm called ATLR for the gigabit-level communication in the high-speed road environment. Finally, we discuss how the THz transmission helps relieve the interference problem and provide extra data to support various instructions in autonomous vehicles

Sb-, Dy-, and Eu-doped oxyfluoride silicate glasses for light emitting diodes

International audienceA series of Sb-, Dy-, and Eu-doped oxyfluoride silicate glasses for light emitting diodes (LEDs) applications were prepared via the melt-quenching method, and studied by a) photoluminescence emission and excitation spectra, b) decay curves, c) Commission Internationale de L'Eclairage (CIE) chromaticity coordinates, and d) correlated color temperatures (CCTs). We discover the energy transfer from Sb3+ to Dy3+ ions occurs in Sb/Dy co-doped glass. We also find the emission behavior of Sb3+ single doped glass is dependent on the excitation wavelength. Furthermore, the white light emission can be achieved in Sb/Dy/Eu co-doped oxyfluoride silicate glasses under ultraviolet (UV) light excitation. The results presented here demonstrate that the as-prepared Sb/Dy/Eu doped oxyfluoride silicate glasses may serve as a potential candidate for LEDs-based lightin

Improved Extreme Learning Machine and Its Application in Image Quality Assessment

Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted) ELM, and propose a modified (weighted) extreme learning machine (M-ELM and M-WELM). Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment

Recent Progress of Catalytic Cathodes for Lithium-oxygen Batteries

Lithium-oxygen batteries are among the most promising electrochemical energy storage systems, which have attracted significant attention in the past few years duo to its far more energy density than lithium-ion batteries. Lithium oxygen battery energy storage is a reactive storage mechanism, and the discharge and charge processes are usually called oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Consequently, complex systems usually create complex problems, lithium oxygen batteries also face many problems, such as excessive accumulation of discharge products (Li2O2) in the cathode pores, resulting in reduced capacity, unstable cycling performance and so on. Cathode catalyst, which could influence the kinetics of OER and ORR in lithium oxygen (Li-O2) battery, is one of the decisive factors to determine the electrochemical performance of the battery, so the design of cathode catalyst is vitally important. This review discusses the catalytic cathode materials, which are divided into four parts, carbon based materials, metals and metal oxides, composite materials and other materials

Synthesis and electrochemical properties of Sn-SnO2/C nanocomposite

A Sn-Sn02/C nanocomposite was synthesized using the electrospinning method. Thermal analysis was used to determine the content range of Sn and Sn02 in the composite. The composite was characterized by X-ray diffraction, and the particle size and shape in the Sn-SnOiC composite were determined by scanning and transmission electron microscopy. The results show that the Sn-Sn02/C composite takes on a nanofiber morphology, with the diameters of the nanofibers distributed from 50 to 200 nm. The electrOChemical properties of the Sn-SnOiC composite were also investigated. The Sn-SnOiC composite as an electrode material has both higher reversible capacity (887 mAh· g-I). and good cycling performance in lithium-anode ceUs working at room temperature in a 3.0 V to O.Ot V potential window. The Sn-Sn02/C composite could relain a discharge capacity of 546 mAWg aller 30 cycles. The outstanding electrochemical properties of the Sn-SnOiC composite oblained by this method make it possible for Ihis composite to be used as a promising anode material

Estimation of the hydraulic parameters of leaky aquifers based on pumping tests and coupled simulation/optimization: verification using a layered aquifer in Tianjin, China

Accurate estimates of aquifer parameters are necessary for effective groundwater management and for geotechnical engineering applications. Pumping tests may be employed to estimate the hydraulic conductivity in leaky aquifer/aquitard systems. This work introduces a hybrid algorithm with global search capacity (the Genetic algorithm, GA) and local search capacity—the Levenberg-Marquardt (LM) algorithm—coupled with a modified Neuman-Witherspoon solution for leaky aquifers to estimate the aquifer’s hydraulic parameters from pumping-test data. The GA is employed to determine the initial guesses of the aquifer parameter values. The optimal parameter values are then obtained with the LM algorithm, yielding a mixed GA/LM algorithm, herein named GALMA. Results show that the drawdown trends based on the estimated parameters agree well with measured drawdown. The proposed estimation algorithm identifies aquifer parameters with greater reliability than previous approaches. Verification of the GALMA is carried out based on three pumping tests in a layered aquifer in Tianjin, China, and on four historical case studies involving diverse hydrogeological settings. The excellent match between observed drawdown and GALMA-estimated parameters demonstrates the estimation accuracy and superior performance relative to previously reported estimation methods

Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN

Recently, the installation of 5G networks offers a variety of real-time, high-performance and human-oriented customized services. However, the current laying 5G structure is unable to meet all of the growing communication needs by these new emerging services. In this paper, we propose a DQL (Deep Q-learning Network) based intelligent resource management method for 5G architecture, to improve the quality of service (QoS) under limited communication resources. In the environment of network function virtualization (NFV), we aim at improving the efficient usage of spectrum resources. In this two-step solution, our first goal is to guarantee the maximum communication quality with the smallest number of infrastructures. Then, a DQL-based wireless resource allocation algorithm is designed to realize the elaborate operation. Unlike previous studies, our system can provide the allocation policy in a more subdivided way and finally maximize the usage of bandwidth resources. The simulation also shows that our proposed MSIO improves 3.12% in the performance of the maximum coverage importance problem and the ARODQ algorithm improves 4.05% than other standard solutions

Enabling Computational Intelligence for Green Internet of Things: Data-Driven Adaptation in LPWA Networking

With the exponential expansion of the number of Internet of Things (IoT) devices, many state-of-the-art communication technologies are being developed to use the lowerpower but extensively deployed devices. Due to the limits of pure channel characteristics, most protocols cannot allow an IoT network to be simultaneously large-scale and energy-efficient, especially in hybrid architectures. However, different from the original intention to pursue faster and broader connectivity, the daily operation of IoT devices only requires stable and low-cost links. Thus, our design goal is to develop a comprehensive solution for intelligent green IoT networking to satisfy the modern requirements through a data-driven mechanism, so that the IoT networks use computational intelligence to realize self-regulation of composition, size minimization, and throughput optimization. To the best of our knowledge, this study is the first to use the green protocols of LoRa and ZigBee to establish an ad hoc network and solve the problem of energy efficiency. First, we propose a unique initialization mechanism that automatically schedules node clustering and throughput optimization. Then, each device executes a procedure to manage its own energy consumption to optimize switching in and out of sleep mode, which relies on AI-controlled service usage habit prediction to learn the future usage trend. Finally, our new theory is corroborated through real-world deployment and numerical comparisons. We believe that our new type of network organization and control system could improve the performance of all green-oriented IoT services and even change human lifestyle habits

Correction: Estimation of the hydraulic parameters of leaky aquifers based on pumping tests and coupled simulation/optimization: verification using a layered aquifer in Tianjin, China

The correct email address for the corresponding author (Haizuo Zhou) is: [email protected]