Without any prior information about related wireless transmitting nodes, joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task. Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources. In this paper, we propose a particle swarm optimization to estimate multiple co-frequency “blind” source nodes, which is based on the received power data measured by the sensors. To distract the mix signals precisely, a genetic algorithm is applied, and it further improves the estimation performance of the system. The simulation results show the efficiency of the proposed algorithm.

The research on residents’ travel mode choice mainly studies how traffic flows are shared by different traffic modes, which is the prerequisite for the government to establish transportation planning and policy. Traditional methods based on survey or small data sources are difficult to accurately describe, explain and verify residents’ travel mode choice behavior. Recently, thanks to upgrades of urban infrastructures, many real-time location-tracking devices become available. These devices generate massive real-time data, which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively. This paper surveys the current research status of big data-driven residents’ travel mode choice from three aspects: residents’ travel mode identification, acquisition of travel mode influencing factors, and travel mode choice model construction. Finally, the limitations of current research and directions of future research are discussed.

The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories (NVM). NVMs are generally faster than traditional secondary storage devices, write persistently and many offer byte addressing capability. Despite these appealing features, NVMs are difficult to manage and program, which makes it hard to use them as a drop-in replacement for dynamic random-access memory (DRAM). Instead, a majority of modern systems use NVMs through the IO and the file system abstractions. Hiding NVMs under these interfaces poses challenges on how to exploit the new hardware’s performance potential in the existing system software framework. In this article, we survey the key technical issues arisen in this area and introduce several recently developed systems each of which offers novel solutions around these issues.