arXiv subjects

Zhe Zhao

Publications and source records attributed to Zhe Zhao.

149 records · Page 9Linked to original sources

arXiv2016

Spatial Phase and Amplitude Structuring of Beams Using a Combination of Multiple Orthogonal Spatial Functions with Complex Coefficients

Analogous to time signals that can be composed of multiple frequency functions, we use uniquely structured orthogonal spatial modes to create different beam shapes. We tailor the spatial structure by judiciously choosing a weighted combination of multiple modal states within an orthogonal basis set, and we can tunably create beam phase and intensity "shapes" that are not otherwise readily achievable. As an example shape, we use a series of orbital-angular-momentum (OAM) functions with adjustable complex weights to create a reconfigurable spatial region of higher localized power as compared to traditional beam combining. We simulate a structured beam created by coherently combining several orthogonal OAM beams with different complex weights, and we achieve a >10X localized power density enhancement with 19 beams. Additionally, we can create unique shapes by passing a single beam through a specially designed phase and intensity mask that contains the combination of multiple OAM functions each with complex weights. Using this approach, we experimentally demonstrate a ~2.5X localized power density increase when utilizing 9 functions.

physics.optics↗

arXiv2016

Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews

Despite the loss of semantic information, bag-of-ngram based methods still achieve state-of-the-art results for tasks such as sentiment classification of long movie reviews. Many document embeddings methods have been proposed to capture semantics, but they still can't outperform bag-of-ngram based methods on this task. In this paper, we modify the architecture of the recently proposed Paragraph Vector, allowing it to learn document vectors by predicting not only words, but n-gram features as well. Our model is able to capture both semantics and word order in documents while keeping the expressive power of learned vectors. Experimental results on IMDB movie review dataset shows that our model outperforms previous deep learning models and bag-of-ngram based models due to the above advantages. More robust results are also obtained when our model is combined with other models. The source code of our model will be also published together with this paper.

cs.CL↗

arXiv2016

Orbital Angular Momentum-based Space Division Multiplexing for High-capacity Underwater Optical Communications

To increase system capacity of underwater optical communications, we employ the spatial domain to simultaneously transmit multiple orthogonal spatial beams, each carrying an independent data channel. In this paper, we multiplex and transmit four green orbital angular momentum (OAM) beams through a single aperture. Moreover, we investigate the degrading effects of scattering/turbidity, water current, and thermal gradient-induced turbulence, and we find that thermal gradients cause the most distortions and turbidity causes the most loss. We show systems results using two different data generation techniques, one at 1064 nm for 10-Gbit/s/beam and one at 520 nm for 1-Gbit/s/beam, we use both techniques since present data-modulation technologies are faster for infrared (IR) than for green. For the higher-rate link, data is modulated in the IR, and OAM imprinting is performed in the green using a specially-designed metasurface phase mask. For the lower rates, a green laser diode is directly modulated. Finally, we show that inter-channel crosstalk induced by thermal gradients can be mitigated using multi-channel equalisation processing.

physics.optics↗

arXiv2014

Performance Metrics and Design Parameters for a Free-space Communication Link Based on Multiplexing of Multiple Orbital-Angular-Momentum Beams

We study the design parameters for an orbital angular momentum (OAM) multiplexed free-space data link. Power loss, channel crosstalk and power penalty of the link are analyzed in the case of misalignment between the transmitter and receiver (lateral displacement, receiver angular error, or transmitter pointing error). The relationship among the system power loss and link distance, transmitted beam size and receiver aperture size are discussed based on the beam divergence due to free space propagation. We also describe the trade-offs for different receiver aperture sizes and mode spacing of the transmitted OAM beams under given lateral displacements or receiver angular errors. Through simulations and some experiments, we show that (1) a system with a larger transmitted beam size and a larger receiver aperture is more tolerant to the lateral displacement but less tolerant to the receiver angular error; (2) a system with a larger mode spacing, which uses larger OAM charges, suffers more system power loss but less channel crosstalk; thus, a system with a small mode spacing shows lower system power penalty when system power loss dominates (e.g., small lateral displacement or receiver angular error) while that with a larger mode spacing shows lower power penalty when channel crosstalk dominates (e.g., larger lateral displacement or receiver angular error); (3) the effects of lateral displacement and receiver angular error are not necessarily independent; as an example of them combined, the effects of the transmitter pointing error on the system are also investigated.

physics.optics↗

arXiv2014

On the Real-time Prediction Problems of Bursting Hashtags in Twitter

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few become bursting topics. Among the few, only some can be predicted in real-time. In this paper, we take the initiative to conduct a systematic study of a series of challenging real-time prediction problems of bursting hashtags. Which hashtags will become bursting? If they do, when will the burst happen? How long will they remain active? And how soon will they fade away? Based on empirical analysis of real data from Twitter, we provide insightful statistics to answer these questions, which span over the entire lifecycles of hashtags.

cs.SI↗