We propose a slot antenna consisting of a rectangular slot on the ground plane, fed by a microstrip line with a rectangular‐ring‐shaped tuning stub that can be deployed in ultra‐wideband (UWB) communication systems to avoid interference with wireless local area network (WLAN) communication. Our antenna can achieve a single band‐notched property from the 5 GHz frequency to the 6 GHz frequency owing to a controllable band notch that uses L‐ and J‐shaped parasitic elements. The antenna characteristics can be modified to tune the band‐notched property (4 GHz to 5 GHz or 6 GHz to 7 GHz) and the bandwidth of the band notch (1 GHz to 2 GHz). Furthermore, the shifted notch with enhanced width of the band notch from 1 GHz to 1.5 GHz is described in this paper. The UWB slot antenna and L‐ and J‐shaped parasitic elements also provide the band‐rejection function for reference in the WiMAX (3.5 GHz) and WLAN (5 GHz to 6 GHz) regions of the spectrum. Experiment results evidence the return loss performance, radiation patterns, and antenna gains at different operational frequencies. 相似文献
This paper presents a novel multiband microstrip-fed right angle slot antenna design technique for multiple independent frequency bands. The new technique uses various slot sizes at various appropriate positions. We first propose a tri-band slot antenna consisting of three right angle slots. Then, a quad-band slot antenna is developed with four right angle slots which achieves slant ±45° linear polarization, omnidirectional pattern coverage, good antenna gain, and acceptable impedance bandwidths over all the operating frequency range. Moreover, an open-circuited tuning stub is introduced to achieve good impedance matching. Both proposed antennas are designed on a ground plane of RT/duroid 5880 substrate with a thickness of 1.575 mm. The real measurable results show that the desired frequencies used in wireless communication systems, namely, WLAN and WiMax, are efficiently achieved. 相似文献
Colour volumetric data, which is constructed from a set of multi-view images, is capable of providing realistic immersive
experience. However it is not widely applicable due to its manifold increase in bandwidth. This paper presents a novel framework
to achieve scalable volumetric compression. Based on wavelet transformation, data rearrangement algorithm is proposed to compact
volumetric data leading to high efficiency of transformation. The colour data is rearranged using the characteristics of human
visual system. A pre-processing scheme for adaptive resolution is also proposed in this paper. The low resolution overcomes
the limitation of the data transmission at low bitrates, whilst the fine resolution improves the quality of the synthesised
images. Results show significant improvement of the compression performance over the traditional 3D coding. Finally, effect
of using residual coding is investigated in order to show a trade off between the compression and view synthesis performance. 相似文献
This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: (i) content creation, (ii) information analysis, (iii) content enhancement and post production workflows, (iv) information extraction and enhancement, and (v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric—where it is designed to augment, rather than replace, human creativity.