Optimized design of high power mid-infrared Er3+, Pr3+ -codoped ZBLAN fiber laser

Based on the rate equations describing the operation of the Er3+, Pr3+ -codoped ZBLAN fiber lasers with different pump configurations, theoretical calculations that relate to the population characteristics and optimization of CW operation of high power Er3+, Pr3+ :ZBLAN double-clad fiber lasers are presented. Using the measured ET (energy-transfer), ETU (energy-transfer-upconversion) and CR (cross-relaxation) parameters relevant to Er3+, Pr3+ -codoped ZBLAN, a good agreement between the theoretical results from the model and recently reported experimental measurements is obtained. The effects on the slope efficiency of a number of laser parameters including fiber length, reflectance of the output mirror and pumping configuration are quantitatively analyzed and used for the design and optimization of high power Er3+, Pr3+ -codoped ZBLAN fiber lasers.

Optimized design of high power mid-infrared Er3+, Pr3+ -codoped ZBLAN fiber laser

Based on the rate equations describing the operation of the Er3+, Pr3+ -codoped ZBLAN fiber lasers with different pump configurations, theoretical calculations that relate to the population characteristics and optimization of CW operation of high power Er3+, Pr3+ :ZBLAN double-clad fiber lasers are presented. Using the measured ET (energy-transfer), ETU (energy-transfer-upconversion) and CR (cross-relaxation) parameters relevant to Er3+, Pr3+ -codoped ZBLAN, a good agreement between the theoretical results from the model and recently reported experimental measurements is obtained. The effects on the slope efficiency of a number of laser parameters including fiber length, reflectance of the output mirror and pumping configuration are quantitatively analyzed and used for the design and optimization of high power Er3+, Pr3+ -codoped ZBLAN fiber lasers.

An adaptive IEEE 802.15.4 MAC layer transmission scheme for applications with heterogeneous traffic

IEEE 802.15.4 networks has the features of low data rate and low power consumption. It is a strong candidate technique for wireless sensor networks and can find many applications to smart grid. However, due to the low network and energy capacities it is critical to maximize the bandwidth and energy efficiencies of 802.15.4 networks. In this paper we propose an adaptive data transmission scheme with CSMA/CA access control, for applications which may have heavy traffic loads such as smart grids. The adaptive access control is simple to implement. Its compatibility with legacy 802.15.4 devices can be maintained. Simulation results demonstrate the effectiveness of the proposed scheme with largely improved bandwidth and power efficiency. © 2013 International Information Institute.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.