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The purpose of this study is to investigate the relationships between two types of informal ties and radical innovation in the context of China’s university spin-offs and the moderating roles of knowledge breadth and depth in such relationships.

A systematic review of literature on informal ties, internal knowledge base and radical innovation provides the theoretical foundation of the research framework and hypotheses. Using a sample of 158 China’s university spin-offs, the authors conduct a regression analysis on the theoretical framework and hypotheses.

The results show that business and university ties are positively related to radical innovation. Moreover, the effects of business and university ties on radical innovation are contingent on knowledge breadth and depth in opposite ways. In particular, the positive effect of business ties on radical innovation depends significantly on internal knowledge depth rather than on knowledge breadth, and the positive effect of university ties on radical innovation will be affected by internal knowledge breadth rather than knowledge depth.

Managers of university spin-offs must examine informal ties they already have and identify their nature, content and embedded advantages and promptly adjust their strategy of informal ties to adapt to their firms’ internal knowledge base.

This study highlights the positive role of managers’ personal connections with different external parties in facilitating radical innovation and advances the understanding of informal ties by proposing that the effects of informal ties on radical innovation are contingent on a firm’s internal knowledge base in the context of China’s university spin-offs.

Magnetic sensors have recently been proposed for parking occupancy detection. However, there has adjacent interference problem, i.e. the magnetic signal is easy to be interfered by the vehicles which are parking on adjacent spaces. The purpose of this paper is to propose a sensing algorithm to eliminate the adjacent interference.

The magnetic signals are converted to the pattern representation sequences, and the similarity is calculated using the pattern distance. The detection algorithm includes two levels: local decision and data fusion. In the local decision level, the sampled signals can be divided into three classes: vacant, occupied and uncertain. Then a collaborative decision is used to fusion the signals which belong to the uncertain class for the second level.

An experiment system included 60 sensor nodes that were deployed on bay parking spaces. Experiment results show that the proposed algorithm has better detection accuracy than existing algorithms.

This paper proposes a data fusion algorithm to eliminate adjacent interference. To balance the energy consumption and detection accuracy, the algorithm includes two levels: local decision and data fusion. In most of cases, the local decision can obtain the accurate detection result. Only the signals that cannot be correctly detected at the local level need data fusion operation.