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Microwave, MTT-S International Symposium

作者： Yangyang Guan Shicong Wang Manos M. Tentzeris Yuanan Liu Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University China Agile Technologies for High-performance Electromagnetic Novel Applications Georgia Institute of Technology USA

In this paper, a spherical helix module based on 3D printing and microfluidic technologies was fabricated, tested and applied to drug delivery applications. A liquid metal alloy is introduced to enhance the sensitivity of the complex and drug-/liquid-reconfigurable helix topology. Moreover, the influence of the dimensions of the hemisphere inscribed by the helical turns on the resonant frequency of the structure is researched. The microfluidic channels with 1 mm radius are shaped in the form of double semispherical helix using a Form-2 3D printer. To fulfill effectively the liquid metal (LM) injection, movement and recycle for reuse, a syringe pump is integrated to flexibly control the flow of the fluid. Besides, the proof-of-concept prototype achieves excellent radiation gain and impedance matching. The realized sensitivity is 0.96 mL/GHz and 0.33 mL/GHz corresponding with the frequency shift from 2.28 to 2.01 GHz and 2.01 to 1.65 GHz.

关键词： Resonant frequency Monitoring Microfluidics Liquids Drugs Mathematical model Three-dimensional printing

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Learning How to Avoiding Obstacles for End-to-End Driving with Conditional Imitation Learning 19

Learning How to Avoiding Obstacles for End-to-End Driving wi...

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Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning

作者： Enwei Zhang Hongtu Zhou Yongchao Ding Junqiao Zhao Chen Ye The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering P. R. China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering P. R. China

ISBN: (纸本)9781450372213

Obstacle avoiding is one of the most complex tasks for autonomous driving systems, which was also ignored by many cutting-edge end-to-end learning-based methods. The difficulties stem from the integrated process of detection and interpretation of environment and obstacles and generation of proper behaviors. We make the use of CARLA, a simulator for autonomous driving research, and collect massive human drivers' reactions to obstacles on road subjecting to given driving commands, i.e. follow, go straight, turn left and turn right for about 6 hours. A behavior-Cloning neural network architecture is proposed with the modified loss that enlarge the effects of errors for steer, which indicates the benefit to high an accuracy. We found the data augmentation of the image is crucial to the training of the proposed network. And a reasonable limit allows avoiding unexpected stop. The experiments demonstrate 3 obstacle avoidance cases: for the same type as the training dataset, other automobile and two-wheeled vehicles. Finally, the CARLA benchmark is also tested.

关键词： imitation learning obstacle avoidance end-to-end autonomous vehicles

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An Improved Convolutional Neural Network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

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IEEE International Conference on systems, Man, and Cybernetics

作者： JunQi Zhang ZeZhou Li Qi Kang MengChu Zhou Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Department of Control Science and Engineering Tongji University Department of Electrical and Computer Engineering New Jersy Institute of Technology

ISBN: (纸本)9781479938414

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

关键词： Learning Automata Optimal subset Pursuit algorithm

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SmartMote: Energy and VoI aware solar-powered sensor network design for environment monitoring

SmartMote: Energy and VoI aware solar-powered sensor network...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Shao-Jie Tang Cheng Bo Xiang-Yang Li Yunhao Li Xiaohua Xu Jing Yuan Department of Computer Science Illinois Institute of Technology USA The Key Laboratory of Embedded System and Service Computing Tongji University China Tsinghua University Beijing Beijing CN Department of Computer Science University of Illinois at Chicago USA

Due to advances in low power micro-sensor technology, energy harvesting techniques, we can now build large scale solar-powered sensor networks to support long-running operations. Solar powered sensors often harvest variable amounts of energy in different weather conditions. Then a primary requirement for an efficient and a long-running solar-powered sensor system is to adapt to changing environment conditions and resources, and to gather as much valuable data as possible. Sensing and collecting data at a constant rate, without taking into account energy availability or data deliverability, will either drain the battery or waste resources. In this work, we design and test a highly efficient and robust solar-powered system SmartMote; and we further present an energy and value of information (VoI) aware routing strategy, that balances the rates of sensing with packet delivery for SmartMote. SmartMote achieves fairness and near maximum utility across the network. We deploy SmartMote in a forest with 100 sensors in order to monitor the humidity, temperature and luminance intensity. Our experimental results corroborate our design.

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Object rigidity and reflectivity identification based on motion analysis

Object rigidity and reflectivity identification based on mot...

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IEEE International Conference on Image Processing

作者： Di Zang Paul R. Schrater Katja Doerschner Department of Computer Science and Engineering University of Minnesota USA Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji China National Research Center of Magnetic Resonance and Department of Psychology Bilkent University Turkey

Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object.

关键词： Optical imaging Adaptive optics Mutual information Shape Computer vision Integrated optics Object recognition

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Social incentive policies to engage commercial building occupants in demand response

Social incentive policies to engage commercial building occu...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Sisi Li Kun Deng MengChu Zhou Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL USA Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Occupants' comfort is the primary target in a building operation. However their efforts are often neglected and ruled out from traditional control strategies of energy-efficient building management systems. Occupant-engaged control strategies have recently attracted many research attentions and demonstrated great potentials for energy saving. With them, occupants' behavior is incorporated into the closed-loop control methods in which their initiatives actively contribute to building services and energy utility by explicitly expressing their preferences. This work proposes an occupant-engaged demand response (DR) strategy for building automation in which occupants are actively engaged to adapt their energy consumption in response to incentive opportunities designed by facility managers. A model-based study and a Nash-Equilibrium-based solution are provided to assist facility managers with the design of social incentive policies to promote occupant participation, along with the guarantee of lucrativeness for a DR event.

关键词： Games Electricity Load management Building automation Companies

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Structure-Aware Generative Adversarial Network for Text-to-Image Generation

Structure-Aware Generative Adversarial Network for Text-to-I...

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IEEE International Conference on Image Processing

作者： Wenjie Chen Zhangkai Ni Hanli Wang Department of Computer Science and Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai P. R. China

Text-to-image generation aims at synthesizing photo-realistic images from textual descriptions. Existing methods typically align images with the corresponding texts in a joint semantic space. However, the presence of the modality gap in the joint semantic space leads to misalignment. Meanwhile, the limited receptive field of the convolutional neural network leads to structural distortions of generated images. In this work, a structure-aware generative adversarial network (SaGAN) is proposed for (1) semantically aligning multimodal features in the joint semantic space in a learnable manner; and (2) improving the structure and contour of generated images by the designed content-invariant negative samples. Experimental results show that SaGAN achieves over 30.1% and 8.2% improvements in terms of FID on the datasets of CUB and COCO when compared with the state-of-the-art approaches.

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Petri net modeling and one-wafer scheduling of single-arm multi-cluster tools

Petri net modeling and one-wafer scheduling of single-arm mu...

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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Zhu, Qinghua Wu, Naiqi Qiao, Yan Zhou, Mengchu School of Computer Guangdong University of Technology Guangzhou 510006 China Macau University of Science and Technology China Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou 510006 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States

ISBN: (纸本)9781479915156

It is very challenging to schedule a multi-cluster tool for it needs to coordinate the activities of multiple robots. This paper studies one-wafer cyclic scheduling for multi-cluster tools whose bottleneck cluster tool is process-bound. A Petri net model is developed to describe the system. Based on the model, it is shown that, for any multi-cluster tool whose bottleneck cluster tool is process-bound, there is always a one-wafer cyclic schedule. Then, a method is presented to find the minimal cycle time and the optimal one-wafer cyclic schedule. The method requires only simple calculation. An example illustrates the application and effectiveness of the proposed method. © 2013 IEEE.

关键词： Scheduling

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SelectCast: Scalable Data Aggregation Scheme in Wireless Sensor Networks

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IEEE Transactions on Parallel and Distributed systems 2012年第10期23卷 1958-1969页

作者： Cheng Wang Changjun Jiang Shaojie Tang Xiang-Yang Li Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science and Engineering Tsinghua National Laboratory for Information Science and Technology (TNLIST) University of Tongji China

In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density \lambda \in [1,n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficiency refers to the ratio of the number of the sensors whose data have been gathered to the total number of sensors. Specifically, we design two efficient aggregation schemes, called single-hop-length (SHL) scheme and multiple-hop-length (MHL) scheme. By novelly integrating these two schemes, we theoretically prove that our protocol achieves the optimal tradeoffs, and derive the optimal aggregation throughput depending on a given threshold value (lower bound) on gathering efficiency. Particularly, we show that under the MHL scheme, for a practically important set of symmetric functions called divisible perfectly compressible (DPC) functions, including the mean, max, and various kinds of indicator functions, etc., the data from \Theta (n) sensors can be aggregated to the sink at the throughput of a constant order \Theta (1), implying that, our MHL scheme is indeed scalable.

关键词： Throughput Wireless sensor networks Temperature measurement Protocols Lattices Temperature sensors

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