This paper presents a flexible microstrip low-pass filter (LPF) fabricated on a polyimide film substrate. The proposed flexible microstrip LPF with ten cascaded asymmetrical Pi-shaped defected ground structure (DGS) resonators are designed, simulated, manufactured, and measured. The simulation and experiment results demonstrate that the designed flexible microstrip LPF has a very sharp transition band, an ultra-wide stop-band (SB) and high rejection in SB performance compared with the existed microstrip LPFs with symmetrical and asymmetrical DGS. The proposed flexible LPF with ten cascaded resonators is with a compact size of 100 mm $\times2.6$ mm $\times0.254$ mm, a very low insertion loss of less than 1.9 dB under 2.2 GHz, and a wide SB from 2.7 to 12 GHz with the rejection of larger than 50 dB. The proposed flexible LPF has the potential to be used in wireless terminals to replace the traditional RF coaxial cable because it is very thin and has good transmission and filtering functions.

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named as P-CSREC (Personalized Cloud Service Recommendation), based on the characterization of heterogeneous information network, the use of association rule mining and the modeling and clustering of user interests. Firstly, a similarity measure is defined to improve the AvgSim (Average Similarity) measure by inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Secondly, the traditional KHM (K-Harmonic Means) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a FP-Growth (Frequent Pattern Growth) association rules algorithm is proposed to address the issue and limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user's score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy.