Institution
Beijing University of Posts and Telecommunications
Education•Beijing, Beijing, China•
About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.
Papers published on a yearly basis
Papers
More filters
••
01 Jun 2018TL;DR: In this article, the authors introduce the MKL into KCF in a different way than MKCF, which alleviates the negative mutual interference of different kernels significantly and improves the performance.
Abstract: Correlation filter (CF) based trackers are currently ranked top in terms of their performances. Nevertheless, only some of them, such as KCF [26] and MKCF [48], are able to exploit the powerful discriminability of non-linear kernels. Although MKCF achieves more powerful discriminability than KCF through introducing multi-kernel learning (MKL) into KCF, its improvement over KCF is quite limited and its computational burden increases significantly in comparison with KCF. In this paper, we will introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL version of CF objective function with its upper bound, alleviating the negative mutual interference of different kernels significantly. Our novel MKCF tracker, MKCFup, outperforms KCF and MKCF with large margins and can still work at very high fps. Extensive experiments on public data sets show that our method is superior to state-of-the-art algorithms for target objects of small move at very high speed.
86 citations
••
TL;DR: In this paper, the top electrodes from Au/Ti to single-layer graphene were replaced with transparent electrodes, which not only allowed the majority of the incident light to reach the contact area but also offered an easy carrier transport channel when the electron-holes were separated.
86 citations
•
TL;DR: This article examines recent research in molecular communications from a telecommunications system design perspective, and focuses on channel models and state-of-the-art physical layer techniques.
Abstract: This article examines recent research in molecular communications from a telecommunications system design perspective. In particular, it focuses on channel models and state-of-the-art physical layer techniques. The goal is to provide a foundation for higher layer research and motivation for research and development of functional prototypes. In the first part of the article, we focus on the channel and noise model, comparing molecular and radio-wave pathloss formulae. In the second part, the article examines, equipped with the appropriate channel knowledge, the design of appropriate modulation and error correction coding schemes. The third reviews transmitter and receiver side signal processing methods that suppress inter-symbol-interference. Taken together, the three parts present a series of physical layer techniques that are necessary to producing reliable and practical molecular communications.
86 citations
••
TL;DR: In this paper, a hierarchical decision model is used to evaluate coal, petroleum, natural gas, nuclear energy and renewable energy resources as energy alternatives for China through use of a Hierarchical Decision Model.
86 citations
•
TL;DR: A reinforcement learning based race balance network (RL-RBN) is proposed that successfully mitigates racial bias and learns more balanced performance and two ethnicity aware training datasets are provided.
Abstract: Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses. A reinforcement learning based race balance network (RL-RBN) is proposed. We formulate the process of finding the optimal margins for non-Caucasians as a Markov decision process and employ deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function. Guided by the agent, the skewness of feature scatter between races can be reduced. Besides, we provide two ethnicity aware training datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be utilized to study racial bias from both data and algorithm aspects. Extensive experiments on RFW database show that RL-RBN successfully mitigates racial bias and learns more balanced performance for different races.
86 citations
Authors
Showing all 39925 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jie Zhang | 178 | 4857 | 221720 |
Jian Li | 133 | 2863 | 87131 |
Ming Li | 103 | 1669 | 62672 |
Kang G. Shin | 98 | 885 | 38572 |
Lei Liu | 98 | 2041 | 51163 |
Muhammad Shoaib | 97 | 1333 | 47617 |
Stan Z. Li | 97 | 532 | 41793 |
Qi Tian | 96 | 1030 | 41010 |
Xiaodong Xu | 94 | 1122 | 50817 |
Qi-Kun Xue | 84 | 589 | 30908 |
Long Wang | 84 | 835 | 30926 |
Jing Zhou | 84 | 533 | 37101 |
Hao Yu | 81 | 981 | 27765 |
Mohsen Guizani | 79 | 1110 | 31282 |
Muhammad Iqbal | 77 | 961 | 23821 |