A Wideband Coaxial-to-Ridge waveguide Adaptor

A Coaxial-to-ridge waveguide adaptor covering the entire K and Ka band has been demonstrated in this article. The adaptor is in the form of asymmetric double ridge waveguides and characteristic impedances of each step is determined by Chebyshev polynomial. A new method of calculating the characteristic impedance of the asymmetric double ridge waveguide is presented and the wideband adaptor is designed on this basis. The simulated results for the proposed adaptor in HFSS show that the return loss is better than 17.8dB in the entire K and Ka band and the insertion loss is better than 0.1dB. The simulated results for the back-to-back confi guration show that return loss is better than 15 dB and insertion loss is better than 0.2dB. To demonstrate its performance, the adaptor is fabricated and then measured on the vector network analyser. The measured results show that the average insertion loss of the adaptor is about 1dB in the whole band

Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification

Multi-Label Remote Sensing Image Classification (MLRSIC) has received increasing research interest. Taking the cooccurrence relationship of multiple labels as additional information helps to improve the performance of this task. Current methods focus on using it to constrain the final feature output of a Convolutional Neural Network (CNN). On the one hand, these methods do not make full use of label correlation to form feature representation. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called Semantic Interleaving Global Channel Attention (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the data set. The label co-occurrence graph is used as the input of the Graph Neural Network (GNN) to generate optimal feature representations. Then, the semantic features and visual features are interleaved, to guide the feature expression of the image from the original feature space to the semantic feature space with embedded label relations. SIGNA triggers global attention of feature maps channels in a new semantic feature space to extract more important visual features. Multihead SIGNA based feature adaptive weighting networks are proposed to act on any layer of CNN in a plug-and-play manner. For remote sensing images, better classification performance can be achieved by inserting CNN into the shallow layer. We conduct extensive experimental comparisons on three data sets: UCM data set, AID data set, and DFC15 data set. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. It is worth mentioning that the codes of this paper will be open to the community for reproducibility research. Our codes are available at https://github.com/kyle-one/SIGNA.Comment: 14 pages, 13 figure

Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning

This paper considers the problem of resource allocation in stream processing, where continuous data flows must be processed in real time in a large distributed system. To maximize system throughput, the resource allocation strategy that partitions the computation tasks of a stream processing graph onto computing devices must simultaneously balance workload distribution and minimize communication. Since this problem of graph partitioning is known to be NP-complete yet crucial to practical streaming systems, many heuristic-based algorithms have been developed to find reasonably good solutions. In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data. We, for the first time, propose to leverage graph embedding to learn the structural information of the stream processing graphs. Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTM-based encoder-decoder model, in about 70% of the test cases.Comment: Accepted by AAAI 202

The Study of Clustering of Taiwanese Tourists\u27 Motivations to Hong Kong

Abstract Driven by the political and economic forces of cross-strait, Taiwan has become one of the major source markets for Hong Kong tourism industry since 1987. The major purposes of this study were to investigate the following factors (1) The influential factors of travel motivation, (2) The clusters of travel motivations, (3) The marketing segmentation of clusters of Taiwanese tourists to visit Hong Kong. Through ten travel agents, self-report surveys were distributed to collect data from 366 Taiwanese travelers. Hence, four push factors and six pull factors were identified as travel motivations through the factor analysis. Combined with the cluster analysis; five new groups were founded. Finally, five clusters which process unique profiles (location difference, visiting frequency, travel satisfaction, and destination loyalty) were addressed. The suggestions of developing effective market strategies to attract Taiwanese tourists to Hong Kong were also provided

On the performance of expected transmission count (etx) for wireless mesh networks,”

ABSTRACT The Expected Transmission Count (ETX) metric is an advanced routing metric for finding high-throughput paths in multi-hop wireless networks. However, it has been determined that ETX is not immune to load sensitivity and route oscillations in a single radio environment. Route oscillations refer to the situation where packet transmission switches between two or more routes due to congestion. This has the effect of degrading performance of the network, as the routing protocol may select a non optimal path. In this paper we avoid the route oscillation problem using a route stabilization technique which forces data transmission on a fixed route. We implement this solution in a popular routing protocol, AODV, by disabling both error messages and periodic updating messages. Therefore, packet transmissions will stay on the routes initially found by AODV. ETX is compared with a widely used routing metric, HOPS, for reference purposes. We find ETX greatly improves initial route selection in AODV compared to HOPS in networks in which only single flows exists. For networks in which there are multiple simultaneous flows, ETX behaves similar to HOPS in initial route selection. Although the known cause of performance degradation is eliminated, the ETX metric still shows anomalous behavior. We determine that a major cause of the poor performance of ETX is additional collisions due to extra overhead. We propose a modified solution in which we repeatedly broadcast RREQ (Route Request) packets. Simulation results show that our modified solution improves ETX in the initial route selection in both single flows and multiple flows cases

Human induced pluripotent stem cell-derived cardiac myocytes and sympathetic neurons in disease modelling

Human induced pluripotent stem cells (hiPSC) offer an unprecedented opportunity to generate model systems that facilitate a mechanistic understanding of human disease. Current differentiation protocols are capable of generating cardiac myocytes (hiPSC-CM) and sympathetic neurons (hiPSC-SN). However, the ability of hiPSC-derived neurocardiac co-culture systems to replicate the human phenotype in disease modelling is still in its infancy. Here, we adapted current methods for efficient and replicable induction of hiPSC-CM and hiPSC-SN. Expression of cell-type-specific proteins were confirmed by flow cytometry and immunofluorescence staining. The utility of healthy hiPSC-CM was tested with pressor agents to develop a model of cardiac hypertrophy. Treatment with angiotensin II (AngII) resulted in: (i) cell and nuclear enlargement, (ii) enhanced fetal gene expression, and (iii) FRET-activated cAMP responses to adrenergic stimulation. AngII or KCl increased intracellular calcium transients in hiPSC-SN. Immunostaining in neurocardiac co-cultures demonstrated anatomical innervation to myocytes, where myocyte cytosolic cAMP responses were enhanced by forskolin compared with monocultures. In conclusion, human iPSC-derived cardiac myocytes and sympathetic neurons replicated many features of the anatomy and (patho)physiology of these cells, where co-culture preparations behaved in a manner that mimicked key physiological responses seen in other mammalian systems. This article is part of the theme issue 'The heartbeat: its molecular basis and physiological mechanisms'