The base transceiver stations (BTS) are telecom infrastructures that facilitate wireless communication between the subscriber device and the telecom operator networks. They are deployed in suitable places having a lot of freely propagating ambient radio frequency (RF) and solar energies. This paper is aimed at converting received ambient environmental energy into usable electricity to power the stations. We proposed a hybrid energy harvesting system that can collect energy from RF and solar energies at the same time. The sources are combined to provide to a significant amount, to contribute to operational expenditures that reduce energy costs, and to improve the energy efficiency of the base station sites in rural areas from the most common renewable resources since the base stations are major consumers of cellular networks. The hybrid systems are designed with circuits, simulated, and compared to show their good performance to the base stations. PSIM, PROTEUS, and MATLAB software are used to simulate for evaluating the voltage and the current output of the hybrid systems that meet the power requirements. The design and simulation results show the feasibility of our proposed method with the battery storage that can be deployed not only in real base stations but also for other electrical operated systems.

Software developers often make use of the online forums such as StackOverflow (SO) to learn how to use software libraries and their APIs. However, the code snippets in such a forum often contain undeclared, ambiguous, or largely unqualified external references. Such declaration ambiguity and external reference ambiguity present challenges for developers in learning to correctly use the APIs. In this paper, we propose StatType, a statistical approach to resolve the fully qualified names (FQNs) for the API elements in such code snippets. Unlike existing approaches that are based on heuristics, StatType has two well-integrated factors. We first learn from a large training code corpus the FQNs that often co-occur. Then, to derive the FQN for an API name in a code snippet, we use that knowledge and also leverage the context consisting of neighboring API names. To realize those factors, we treat the problem as statistical machine translation from source code with partially qualified names to source code with FQNs of the APIs. Our empirical evaluation on real-world code and StackOverflow posts shows that StatType achieves very high accuracy with 97.6% precision and 96.7% recall, which is 16.5% relatively higher than the state-of-the-art approach.

When being trained on API documentation and tutorials, Word2vec produces vector representations to estimate the relevance between texts and API elements. However, existing Word2vec-based approaches to measure document similarities aggregate Word2vec vectors of individual words or APIs to build the representation of a document as if the words are independent. Thus, the semantics of API descriptions or code fragments are not well represented.