444 research outputs found
Technical Rate of Substitution of Spectrum in Future Mobile Broadband Provisioning
Dense deployment of base stations (BSs) and multi-antenna techniques are
considered key enablers for future mobile networks. Meanwhile, spectrum sharing
techniques and utilization of higher frequency bands make more bandwidth
available. An important question for future system design is which element is
more effective than others. In this paper, we introduce the concept of
technical rate of substitution (TRS) from microeconomics and study the TRS of
spectrum in terms of BS density and antenna number per BS. Numerical results
show that TRS becomes higher with increasing user data rate requirement,
suggesting that spectrum is the most effective means of provisioning extremely
fast mobile broadband.Comment: 5 pages, 5 figures, conferenc
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors
Disorder Improves Light Absorption in Thin Film Silicon Solar Cells with Hybrid Light Trapping Structure
We present a systematic simulation study on the impact of disorder in thin film silicon solar cells with hybrid light trapping structure. For the periodical structures introducing certain randomness in some parameters, the nanophotonic light trapping effect is demonstrated to be superior to their periodic counterparts. The nanophotonic light trapping effect can be associated with the increased modes induced by the structural disorders. Our study is a systematic proof that certain disorder is conceptually an advantage for nanophotonic light trapping concepts in thin film solar cells. The result is relevant to the large field of research on nanophotonic light trapping which currently investigates and prototypes a number of new concepts including disordered periodic and quasiperiodic textures. The random effect on the shape of the pattern (position, height, and radius) investigated in this paper could be a good approach to estimate the influence of experimental inaccuracies for periodic or quasi-periodic structures
- …