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Outphasing power amplifiers (OPAs) can be used in wireless information and power transfer systems due to their excellent conversion efficiency. With the rapid advancement of technology, there is a strong demand for OPAs to achieve wideband and multi-band operations to enhance frequency coverage. This paper provides a comprehensive review of the research progress on the development of broadband and multi-band board-level OPAs. Firstly, the paper introduces the fundamental components of an OPA. Then, the paper delves into a detailed analysis of the load modulation of OPAs. Subsequently, previous works on the circuit topology and design methods for broadband and multi-band OPAs are discussed and compared. The paper highlights various design strategies and their respective advantages and disadvantages. Finally, the paper explores some potential design methods for broadband and multi-band OPAs, discussing emerging technologies and highlighting potential research directions. [ABSTRACT FROM AUTHOR]

Similar to a Doherty power amplifier (DPA), a sequential power amplifier (SPA) is mainly composed of a main amplifier, an auxiliary amplifier and a combiner. However, SPA breaks the bandwidth limitation of the impedance inverter in the DPA, and also simplifies the design procedure. Since the main amplifier has no load modulation, it is easy for the SPA to realize broadband operation and improve the output back-off (OBO) power range. Therefore, SPA has great advantages and potential in expanding bandwidth, improving drain efficiency and expanding the back-off range of a power amplifier simultaneously. This paper describes the evolution and classification of the SPA. First, the basic theory of the SPA is reviewed. Then, some two-way SPAs using coupler and circulator as a power combiner are discussed. Thirdly, the latest popular sequential load modulated balanced amplifier is overviewed. [ABSTRACT FROM AUTHOR]

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement. [ABSTRACT FROM AUTHOR]