Wideband characteristics of density tapered array antennas

In this paper, wideband characteristics of density tapered arrays are clarified by comparing directly the array factors and radiation patterns of 3 tapered arrays structures with array factors and radiation patterns of equally spaced arrays. Calculated results for a density tapered distribution array consisting of 30 elements claims that the array can perform within a bandwidth of 2.5:1 with grating lobe levels lower than -7.8 dB. Additionally, this paper shows a method of determining the effectiveness of unequal spacing arrays in the design of actual antennas. The method is based on calculation and analysis of input impedance of array elements caused by mutual coupling effects among array elements

ESTIMATION OF THE EFFECT OF NONLINEAR HIGH POWER AMPLIFIER IN M-QAM RADIO RELAY SYSTEMS

The estimation of the effect of both linear and nonlinear distortions in M-QAM radio systems requires either complicated analytical calculation or very long run of simulation. In this paper a new parameter of nonlinearity is produced and the relationship between this parameter and the signal to noise ratio degradation (S N RD) caused by the separated effect of nonlinear HPA (High Power Amplifier) is presented. In addition, the estimation of the simultaneous effect of linear and nonlinear distortions is discussed and a procedure to calculate the upper bound of BER (bit-error ratio) for this case is also presented

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

DESIGNING A GEODATABASE MODEL FOR URBAN INFORMATION SYSTEM AT THE BASIC LEVEL (Case Study in Nguyen Du Ward, Hai Ba Trung District, Hanoi City)

Joint Research on Environmental Science and Technology for the Eart

Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments

We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing temporal order-preserving priors for obtaining the temporal similarity score between the videos. Moreover, we introduce a few-shot video classification framework that leverages the above appearance and temporal similarity scores across multiple steps, namely prototype-based training and testing as well as inductive and transductive prototype refinement. To the best of our knowledge, our work is the first to explore transductive few-shot video classification. Extensive experiments on both Kinetics and Something-Something V2 datasets show that both appearance and temporal alignments are crucial for datasets with temporal order sensitivity such as Something-Something V2. Our approach achieves similar or better results than previous methods on both datasets. Our code is available at https://github.com/VinAIResearch/fsvc-ata.Comment: Accepted to ECCV 202

A Novel High-Speed Architecture for Integrating Multiple DDoS Countermeasure Mechanisms Using Reconfigurable Hardware

In this paper, we proposed a novel high-speed architecture to incorporate multiple stand-alone DDoS countering mechanisms. The architecture separates DDoS filtering mechanisms, which are algorithms, out of packet decoder, which is the basement. The architecture not only helps developers to give more concentration on optimizing algorithms but also integrate multiple algorithms to achieve more efficient DDoS defense mechanism. The architecture is implemented on reconfigurable hardware, which helps algorithms to be flexibly changed or updated. We implemented and experimented the system using NetFPGA 10G board with incorporation of Port Ingress/Egress Filtering and Hop-Count Filtering to classify IP spoofing packets. The synthesis results show that the system runs at 118.907 MHz, utilizes 38.99% Registers, and 44.75% BlockRAMs/FIFOs of the NetFPGA 10G board. The system achieves the detection rate of 100% with false negative rate at 0%, and false positive rate closed to 0.16%. The experimental results prove that the system achieves packet decoding throughput at 9.869 Gbps in half-duplex mode and 19.738 Gbps in full-duplex mode