High-density, end-to-end optoelectronic integration and packaging for digital-optical interconnect systems (Invited Paper)

2005 ◽  
Author(s):  
Gee-Kung Chang ◽  
Daniel Guidotti ◽  
Zhaoran Rena Huang ◽  
Lixi Wan ◽  
Jianjun Yu ◽  
...  
Keyword(s):  
High Density ◽  
Optical Interconnect ◽  
End To End ◽  
Optoelectronic Integration ◽  
Interconnect Systems

Reliability of commercial optical interconnect systems

1997 ◽  
Author(s):  
Nickolaos E. Strifas ◽  
Chandrasekhar Pusarla ◽  
Aristos Christou
Keyword(s):  
Optical Interconnect ◽  
Interconnect Systems

Convolutional Nonlinear Differential Recurrent Neural Networks for Crowd Scene Understanding

2018 ◽  
Vol 12 (04) ◽  
pp. 481-500 ◽  
Author(s):  
Naifan Zhuang ◽  
The Duc Kieu ◽  
Jun Ye ◽  
Kien A. Hua
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Scene Understanding ◽  
Image Data ◽  
High Density ◽  
Temporal Information ◽  
Deep Model ◽  
End To End ◽  
The Individual

With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility, and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, convolutional nonlinear differential recurrent neural networks (CNDRNNs), for crowd scene understanding. CNDRNNs consist of GoogleNet Inception V3 convolutional neural networks (CNNs) and nonlinear differential recurrent neural networks (RNNs). Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, CNDRNN utilizes a unified deep model to optimize the parameters of CNN and RNN hand in hand. It thus has the potential of generating a more harmonious model. The proposed architecture takes sequential raw image data as input, and does not rely on tracklet or trajectory detection. It thus has clear advantages over the traditional flow-based and trajectory-based methods, especially in challenging crowd scenarios of high density and low mobility. Taking advantage of CNN and RNN, CNDRNN can effectively analyze the crowd semantics. Specifically, CNN is good at modeling the semantic crowd scene information. On the other hand, nonlinear differential RNN models the motion information. The individual and increasing orders of derivative of states (DoS) in differential RNN can progressively build up the ability of the long short-term memory (LSTM) gates to detect different levels of salient dynamical patterns in deeper stacked layers modeling higher orders of DoS. Lastly, existing LSTM-based crowd scene solutions explore deep temporal information and are claimed to be “deep in time.” Our proposed method CNDRNN, however, models the spatial and temporal information in a unified architecture and achieves “deep in space and time.” Extensive performance studies on the Violent-Flows, CUHK Crowd, and NUS-HGA datasets show that the proposed technique significantly outperforms state-of-the-art methods.

Design of a SiGe/Si quantum well optical modulator integrated in a silicon-on-insulator waveguide for optical interconnect systems

2004 ◽  
Author(s):  
Delphine Marris ◽  
Daniel Pascal ◽  
Alain Koster ◽  
Laurent Vivien ◽  
Eric Cassan ◽  
...  
Keyword(s):  
Quantum Well ◽  
Optical Interconnect ◽  
Silicon On Insulator ◽  
Optical Modulator ◽  
Interconnect Systems

Multiple-input multiple-output based high density on-chip optical interconnect

2015 ◽  
Author(s):  
Po-Kuan Shen ◽  
Xiaochuan Xu ◽  
Amir Hosseini ◽  
Zeyu Pan ◽  
Ray T. Chen
Keyword(s):  
Multiple Input Multiple Output ◽  
High Density ◽  
Optical Interconnect ◽  
Multiple Input ◽  
Input Multiple Output ◽  
On Chip

A high-density 300-Gbit/s parallel optical interconnect module with efficient optical sub-assembly techniques

2015 ◽  
Author(s):  
Yasunobu Matsuoka ◽  
Yong Lee ◽  
Hideo Arimoto ◽  
Toshiaki Takai ◽  
Norio Chujo ◽  
...  
Keyword(s):  
High Density ◽  
Optical Interconnect

VCSEL arrays for CMOS integrated optical interconnect systems

2001 ◽  
Author(s):  
Rainer Michalzik ◽  
Roger King ◽  
Roland Jaeger ◽  
Steffen Mueller ◽  
Karl J. Ebeling
Keyword(s):  
Optical Interconnect ◽  
Integrated Optical ◽  
Interconnect Systems ◽  
Vcsel Arrays
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