Undergraduate students from California State University, San Bernardino were recruited to examine the effects of working memory training and encoding strategy upon working memory capacity. Participants will be prescreened for low working memory capacity, and then will be tested on a battery of complex span measures. Participants will be divided into several strategy conditions: rehearsal, visual, and control. Then participants will be tested on their verbal working memory both before and after the 20 session n-back working memory training program. Participants are predicted to do the same or worse with the strategy instruction before working memory training while they will improve after training in comparison to control groups. The effects of strategy and training upon working memory capacity were nonsignificant. However, the direction of group differences is consistent with the maximization of individual differences with strategy instruction while cognitive training minimizes individual differences.

Telemedicine extends medical services to patients in remote areas. Due to the trend of population ageing, telemedicine becomes more and more popular. The Internet provides great opportunity for transmitting medical data. However the Internet is unreliable, some uncontrollable network behaviors (such as packet loss, packet delay) influence the quality of medical services greatly. Additionally,most existing medical data formats are designed for storing medical records not for streaming medical data over an unreliable medium. In order to promote the efficiency and achieve the error control of medical sensor data streaming service, this project aims to 1) investigate different streaming protocols and encoding strategies;2) empirically evaluate these encoding strategies and find an optimum choice.In this project, we focus on a home-based electrocardiograph(ECG) sensor monitoring service which requires little overhead, low latency and constant data rate. We develop a framework for testing the efficiency of medical streaming service, and empirically evaluate four forward error correction (FEC) encoding strategies with different erasure codes (XORcod,RS code) and block interleavers. The performances of the four encoding strategies are measured by calculating the Repair Rate &Peak Signal Noise Ratio (PSNR), and the processing times of these encoders are measured as well. Network simulation is used to establish the unreliable network and Network Simulator3 (NS3) is employed as the simulation tool. From the simulation results,we come up the conclusions: Firstly, the average burst error length of a network is the key factor which influences the performance comparison of the four encoding strategies.Secondly, interleaving provides positive impact when packet losses are bursty; while it results in negative impact when packet losses are scattered.Based on the conclusions above, FEC-XOR-Interleaving encoding strategy is the optimum choice for the home-based ECG sensor monitoring scenario,