Optical chemsensors based on etched fibre Bragg gratings in D-shape and multimode fibres

We present an implementation of high-sensitivity optical chemsensors based on FBGs UV-inscribed in D-shape and multimode fibres and sensitized by HF-etching treatment, demonstrating a capability of detecting chemical concentration changes as small as < 0.5%.

High sensitivity biosensor based on dual-peak LPG sensitised by light cladding etching

We demonstrate a high sensitivity biosensor by fine tailoring mode dispersion and sensitivity of dual-peak LPGs using light-cladding-etching method. The etched device has been used to detect concentration of Hemoglobin protein in sugar solution, showing a sensitivity as high as 20nm/1%.

Optical sensor based on hybrid LPG/FBG in D-fiber for simultaneous refractive index and temperature measurement

A dual-parameter optical sensor has been realized by UV-writing a long-period and a Bragg grating structure in D-fiber. The hybrid configuration permits the detection of the temperature from the latter and measuring the external refractive index from the former responses, respectively. The employment of the D-fiber allows as effective modification and enhancement of the device sensitivity by cladding etching. The grating sensor has been used to measure the concentrations of aqueous sugar solutions, demonstrating the potential capability to detect concentration changes as small as 0.01%.

Vectorial bending characteristics of long-period grating written in D-shaped fibre

The curvature- or bend-sensing response of long-period gratings (LPG) UV-inscribed in D-shaped fiber has been investigated experimentally. Strong fiber orientation dependence of the spectral response when such LPGs are subjected to dynamic bending has been observed and is shown to form the basis for new vector sensors.

Low thermal sensitivity grating devices based on ex-45° tilting structure capable of forward-propagating cladding modes coupling

The authors describe a detailed investigation on tilted fiber Bragg grating (TFBG) structures with tilted angles exceeding 45°. In contrast to the backward mode coupling mechanism of Bragg gratings with normal and small tilting structures, the ex-45° TFBGs facilitate the light coupling to the forward-propagating cladding modes. The authors have also theoretically and experimentally examined the mode coupling transition of TFBGs with small, medium, and large tilt angles. In particular, experiments are conducted to investigate the spectra and far-field distribution, as well as temperature, strain, and refractive-index sensitivities of ex-45° devices. It has been revealed that these ex-45° gratings exhibit ultralow thermal sensitivity. As in-fiber devices, they may be superior to conventional Bragg and long-period gratings when the low thermal cross sensitivity is required. © 2006 IEEE.

Real-time detection of DNA interactions with long-period fiber-grating-based biosensor

Using an optical biosensor based on a dual-peak long-period fiber grating, we have demonstrated the detection of interactions between biomolecules in real time. Silanization of the grating surface was successfully realized for the covalent immobilization of probe DNA, which was subsequently hybridized with the complementary target DNA sequence. It is interesting to note that the DNA biosensor was reusable after being stripped off the hybridized target DNA from the grating surface, demonstrating a function of multiple usability. © 2007 Optical Society of America.

A refractometer based on a micro-slot in a fiber Bragg grating formed by chemically assisted femtosecond laser processing

A liquid core waveguide as a refractometer is proposed. Microtunnels were created in standard optical fiber using tightly focused femtoscond laser inscription and chemical etching. A 1.2(h)×l25(d) ×500(1) μm micro-slot engraved along a fiber Bragg grating (FBG) was used to construct liquid core waveguide by filling the slot with index matching oils. The device was used to measure refractive index and sensitivity up to 10-6/pm was obtained. © 2007 Optical Society of America.

Analysis and identification of spamming behaviors in Sina Weibo microblog

Spamming has been a widespread problem for social networks. In recent years there is an increasing interest in the analysis of anti-spamming for microblogs, such as Twitter. In this paper we present a systematic research on the analysis of spamming in Sina Weibo platform, which is currently a dominant microblogging service provider in China. Our research objectives are to understand the specific spamming behaviors in Sina Weibo and find approaches to identify and block spammers in Sina Weibo based on spamming behavior classifiers. To start with the analysis of spamming behaviors we devise several effective methods to collect a large set of spammer samples, including uses of proactive honeypots and crawlers, keywords based searching and buying spammer samples directly from online merchants. We processed the database associated with these spammer samples and interestingly we found three representative spamming behaviors: Aggressive advertising, repeated duplicate reposting and aggressive following. We extract various features and compare the behaviors of spammers and legitimate users with regard to these features. It is found that spamming behaviors and normal behaviors have distinct characteristics. Based on these findings we design an automatic online spammer identification system. Through tests with real data it is demonstrated that the system can effectively detect the spamming behaviors and identify spammers in Sina Weibo.

Observation of Matthew effects in Sina Weibo microblogger

This paper researches on Matthew Effect in Sina Weibo microblogger. We choose the microblogs in the ranking list of Hot Microblog App in Sina Weibo microblogger as target of our study. The differences of repost number of microblogs in the ranking list between before and after the time when it enter the ranking list of Hot Microblog app are analyzed. And we compare the spread features of the microblogs in the ranking list with those hot microblogs not in the list and those ordinary microblogs of users who have some microblog in the ranking list before. Our study proves the existence of Matthew Effect in social network. © 2013 IEEE.

An empirical study on uncertainty identification in social media context

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification. © 2013 Association for Computational Linguistics.

Optical spectrum analyzer using a 45° tilted fiber grating

We report an optical spectrum analyzer utilizing direct side-tapping by a 45° tilted fiber grating. The angular dispersion is analyzed and 45° is found to give highest dispersion. High resolution up to 0.13nm was obtained. © 2012 Optical Society of America.

Cost-effective node monitoring for online hot event detection in sina weibo microblogging

We propose a cost-effective hot event detection system over Sina Weibo platform, currently the dominant microblogging service provider in China. The problem of finding a proper subset of microbloggers under resource constraints is formulated as a mixed-integer problem for which heuristic algorithms are developed to compute approximate solution. Preliminary results show that by tracking about 500 out of 1.6 million candidate microbloggers and processing 15,000 microposts daily, 62% of the hot events can be detected five hours on average earlier than they are published by Weibo.

Chemical sensor using Mach-Zehnder interferometer based on a pair of largely tilted fiber gratings

We propose an in-fiber Mach-Zehnder interferometer formed by a pair of largely tilted fiber gratings. The interference spectral characteristics have been investigated. The experimental results using this device as a chemical sensor have a sensitivity as high as 719nm/RIU. © 2012 OSA.

Graphene-based D-shaped polymer FBG for highly sensitive erythrocyte detection

Graphene-based silica fiber-optic sensors, with high sensitivity, fast response, and low cost, have shown great promise for gas sensing applications. In this letter, by covering a monolayer of p-doped graphene on a D-shaped microstructured polymer fiber Bragg grating (FBG), we propose and demonstrate a novel biochemical probe sensor, the graphene-based D-shaped polymer FBG (GDPFBG). Due to the graphene-based surface evanescent field enhancement, this sensor shows high sensitivity to detect surrounding biochemical parameters. By monitoring the Bragg peak locations of the GDPFBG online, human erythrocyte (red blood cell) solutions with different cellular concentrations ranging from 0 to 104 ppm were detected precisely, with the maximum resolution of sub-ppm. Such a sensor is structurally compact, is clinically acceptable, and provides good recoverability, offering a state-of-the-art polymer-fiber-based sensing platform for highly sensitive in situ and in vivo cell detection applications.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.