Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving/driver assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, novel applications and standards in real world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support, computation algorithms and current development state. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about current regulations and market state of ADAS in China are reviewed in this paper, and other open issues related to ADAS are also introduced in particular.

The business ecosystem has provided a new paradigm for management research. Most research in the field has focused on profit-driven industries, neglecting the area of the circular economy. This research sets out to capture the mechanisms that the leading firm in the circular economy uses to govern its business ecosystem. The research strategy adopted is a longitudinal case study of the largest glass recycling company in Taiwan, Spring Pool Glass. Our findings illustrate that continuous value capture is the key to governing a sustainable business ecosystem in the glass recycling industry. The mechanisms include continuous value capture to enter new markets, using stakeholder networks to enlarge the business ecosystem, brand image and corporate social responsibility, company capabilities and research and development in the recycling process, and reacting to government policy.

Communication has been seen as a significant bottleneck in industrial applications over large-scale networks. To alleviate the communication burden, sign-based optimization algorithms have gained popularity recently in both industrial and academic communities, which is shown to be closely related to adaptive gradient methods, such as Adam. Along this line, this article investigates faster convergence for a variant of sign-based gradient descent, called scaled sign GD, in three cases: First, the objective function is strongly convex; second, the objective function is nonconvex but satisfies the Polyak–Łojasiewicz inequality; third the gradient is stochastic, called scaled sign SGD in this case. For the first two cases, it can be shown that the scaled sign GD converges at a linear rate. For case third, the algorithm is shown to converge linearly to a neighborhood of the optimal value when a constant learning rate is employed, and the algorithm converges at a rate of $O(1/k+1/k^{2}+1/k^{3})$ when using a diminishing learning rate, where $k$ is the iteration number. The results are also extended to the distributed setting by majority vote in a parameter-server framework. Finally, numerical experiments are performed to corroborate the theoretical findings.