Varactor-tunable frequency selective surface with an embedded bias network

A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated.

How Does Focal Firms' Dual Embeddedness in Symbiotic Networks Influence Value Cocreation in Innovation Ecosystems?

Under the current complex and competitive economic environment,more and more firms are embedding themselves into symbiotic networks for value co-creation,since this has become a good strategy to obtain competitive advantages.Thus,it is important to examine the impacts of firms’embeddedness in symbiotic networks on value co-creation in innovation ecosystems.This study analyzes the mechanisms and contextual factors of firms’dual embeddedness(i.e.,relational and knowledge embeddedness)in symbiotic networks and how each influences value co-creation within innovation ecosystems.Using a sample of 1,972 observations,our findings show,firstly,that firms’dual embeddedness in symbiotic networks positively impacts on value co-creation in innovation ecosystems;secondly,that firms’dual embeddedness in symbiotic networks positively impacts on innovation ecosystem resilience;thirdly,that innovation ecosystem resilience mediates the relationships between firms’dual embeddedness in symbiotic networks and value co-creation in innovation ecosystems;and,fourthly,that innovative ecological environments positively moderate the relationship between firms’dual embeddedness and value co-creation in innovation ecosystems.These results not only enrich the theoretical framework concerning value co-creation within innovation ecosystems but also provide managerial suggestions for firms to efficiently enhance the degree of embeddedness in symbiotic networks and build highly resilient innovation ecosystems,thus promoting value co-creation among innovation ecosystem populations.

An ARM-based debugging system for multipole array acoustic logging tools

This paper presents a debugging system for multi-pole array acoustic logging （MPAL） tools. The debugging system proposed in this study can debug the MPAL tool system, sub-system and local electronics. In the test equipment, we have used principal and subordinate structures, and interconnected the host computer and the front-end machine via Ethernet. The front-end machine is based on the ARM7 （advanced reduced instruction set computing （RISC） machine） technique, the processor of which runs an embedded operating system, namely, uClinux OS. We have analyzed the system telecommunication, human-machine interface circuit, transmitter mandrel interface circuit, receiver mandrel interface circuit, and board-level test interface circuit. The software used in the system consists of the embedded front-computer software and the host application software. We have explained in detail the flow chart of the boot loader in the embedded front-computer software. The host application software is composed of four application subroutines, which match with the functional modules of the system hardware. A net communication program based on the server^client mode is implemented by means of socket programming and multi-thread programming. Test results indicate that the data transmission rate of the system is higher than 1 MB/s, which completely meets the current requirements of the data transmission rate between the tool system and the wireline telemetry device. Application of the debugging system, which includes multiple level test methods, shows that the proposed system can fully meet the test requirements of MPAL at various levels.

Image aesthetic quality evaluation using convolution neural network embedded learning

A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.