The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.
Gestures that share similarities in their forms and are related in their meanings, should be easier for learners to recognize and incorporate into their existing lexicon. In that regard, to be more readily accepted as standard by the Deaf and Hard of Hearing community, technical gestures in American Sign Language (ASL) will optimally share similar in forms with their lexical neighbors. We utilize a lexical database of ASL, ASL-LEX, to identify lexical relations within a set of technical gestures. We use automated identification for 3 unique sub-lexical properties in ASL-location, handshape and movement. 𝐸𝑑𝐺𝐶𝑜𝑛 assigned an iconicity rating based on the lexical property similarities of the new gesture with an existing set of technical gestures and the relatedness of the meaning of the new technical word to that of the existing set of technical words. We collected 30 ad hoc crowdsourced technical gestures from different internet websites and tested them against 31 gestures from the DeafTEC technical corpus. We found that 𝐸𝑑𝐺𝐶𝑜𝑛 was able to correctly auto-assign the iconicity ratings 80.76% of the time. 1
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