University of Electro-Communications

About: University of Electro-Communications is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Laser & Robot. The organization has 8041 authors who have published 16950 publications receiving 235832 citations. The organization is also known as: UEC & Denki-Tsūshin Daigaku.

Highly Efficient Persistent Room-Temperature Phosphorescence from Heavy Atom-Free Molecules Triggered by Hidden Long Phosphorescent Antenna.

Abstract: Persistent (lifetime > 100 ms) room-temperature phosphorescence (pRTP) is important for state-of-the-art security and bioimaging applications An unclear relationship between chromophores and physical parameters relating to pRTP has prevented obtaining an RTP yield of over 50% and a lifetime over 1 s Here highly efficient pRTP is reported under ambient conditions from heavy atom-free chromophores A heavy atom-free aromatic core substituted with a long-conjugated amino group considerably accelerates the phosphorescence rate independent of the intramolecular vibration-based nonradiative rate from the lowest excited triplet state One of the designed heavy atom-free dopant chromophores presents an RTP yield of 50% with a lifetime of 1 s under ambient conditions The afterglow brightness under strong excitation is at least 104 times stronger than that of conventional long-persistent luminescence emitters Here it is shown that highly efficient pRTP materials allow for high-resolution gated emission with a size of the diffraction limit using small-scale and low-cost photodetectors

Grounding of word meanings in multimodal concepts using LDA

Abstract: In this paper we propose LDA-based framework for multimodal categorization and words grounding for robots. The robot uses its physical embodiment to grasp and observe an object from various view points as well as listen to the sound during the observing period. This multimodal information is used for categorizing and forming multimodal concepts. At the same time, the words acquired during the observing period are connected to the related concepts using multimodal LDA. We also provide a relevance measure that encodes the degree of connection between words and modalities. The proposed algorithm is implemented on a robot platform and some experiments are carried out to evaluate the algorithm. We also demonstrate a simple conversation between a user and the robot based on the learned model.

Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning

Abstract: To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep $Q$-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice

Abstract: We have developed a variational method to obtain many-body ground states of the Bose–Hubbard model using feedforward artificial neural networks. A fully connected network with a single hidden layer works better than a fully connected network with multiple hidden layers, and a multilayer convolutional network is more efficient than a fully connected network. AdaGrad and Adam are optimization methods that work well. Moreover, we show that many-body ground states with different numbers of particles can be generated by a single network.

Observation of sprites over the Sea of Japan and conditions for lightning‐induced sprites in winter

Abstract: [1] We have succeeded in observing sprites for winter lightning in the Hokuriku area (Japan Sea side) of Japan in the winter of 2001/2002. The optical results on 3 days are compared with the corresponding characteristics of parent (causative) lightning with particular attention to the significant differences between Hokuriku winter lightning and the more widely studied continental lightning. Despite significant differences with Hokuriku winter lightning, we have found nearly the same sprite properties as already observed in the U.S. continent with a significant difference (simpler shape for Hokuriku winter sprite). Then, we have also discussed the criteria for sprite occurrence. Specifically, two similar criteria are found: (1) cloud-to-ground discharges of positive polarity and (2) the presence of a certain threshold in vertical charge moment (200–300 C km) (roughly consistent with that for the U.S. continent). Mesoscale convective systems are not necessary to store the charge necessary for sprites, but the parent Hokuriku winter clouds are substantially smaller than the minimum scale for sprite occurrence in the continental lightning; however, it is larger in area than ordinary summer thunderclouds. However, there may exit another condition such as clustering or self-organizing effect of thunderclouds for sprite production.