In this paper, we propose a novel Hough circle-assisting deep-network inspection scheme (HCADIS), aiming at identifying defects in dense coffee beans. The proposed HCADIS plays a critical role in a camera-based defect removal system to collect defective bean positions for picking all defects off. The idea of the HCADIS is to mix intermediate data from a deep network and a feature engineering method call Hough circle transform for utilizing advantages of both methods in inspecting beans. The Hough circle transform is adopted because it performs quite stable and bean shapes are highly close to circles in nature. A set of core mechanisms are designed for collaboration between the deep network and the Hough circle transform for precisely and accurately inspecting defective beans. Finally, we implement a prototype of the HCADIS and conduct experiments for testing the proposed scheme. The test results reveal that the HCADIS indeed successfully inspect defects among dense beans with superior performance in various metrics. This work provides industrial participants useful experiences for creating deep-learning solutions to bean products in coffee industries.

Tourism navigation systems have become an important research area because they help people strengthen their focus on the quality of the tourism. This paper proposes an adaptive recommendation mechanism that rests on a congestion-aware scheduling method for multigroup travelers on multidestination travels. This recommendation scheme uses the pheromone mechanism of an ant algorithm for group system distribution. In order to reduce congestion in the “visiting multiple destinations” problem that might beset the multiple groups, we present a tour group that could take adaptive recommendations from a system that would yield a high quality tour experience, wherein the group would visit a secondary destination first and then visit the primary destination. Simulation results reveal the strengths of the proposed “adaptive recommendation mechanism” model in terms of decreasing average waiting time, congestion, and the ratio of congestion avoiding to number of groups.

This paper proposes a novel approach to clustering the interests of car drivers, increasing the lifetime of interest groups, and increasing the throughput in vehicle-to-vehicle environments. It develops an interest ontology of cellular automata (CA) clustering using the zone of interest (ZOI) for mobicast communications in vehicular ad hoc network (VANET) environments. The key to the proposed method is to integrate CA clustering with the ontology of users' interests. This paper argues for the use of both an interest profile (ontology) of drivers and information about vehicles to form a group of VANET-related interests. The current study evaluates the performance of the approach by conducting computer simulations. Simulation results reveal the strengths of the proposed CA clustering algorithm in terms of increased group lifetime and increased ZOI throughput for VANETs.

In this paper, we propose a tree-based adaptive broadcasting (TAB) algorithm for data dissemination to improve data access efficiency in vehicle communications. The proposed TAB algorithm first constructs a broadcast tree to determine the broadcast frequency of each data, and splits the broadcast tree into some broadcast wood to generate the broadcast program. In addition, this paper develops an analytical model to derive the mean access latency of the generated broadcast program. In light of the derived result, the bandwidth for both index channel and data channel can be optimally allocated to maximize bandwidth utilization. Furthermore, to evaluate the effectiveness of the proposed strategy, experiments are demonstrated as well. This study argues for the use of an interest profile (ontology) of drivers and information about vehicles to form a group of interest for Vehicle ad hoc Networks (VANETs). The performance of the approach is evaluated by performing computer simulations. From the experimental results, it can be seen that the proposed mechanism is feasible in practice.

The Internet Protocol Television (IPTV) is becoming one of the most promising applications over next generation networks. With the recent released of IEEE802.16d/e, it is capable ensuring high bandwidths and low latency and suitable for delivering multimedia services. In addition, it also provides wide area coverage, mobility support and non-line-of-sight operation. In this paper, we deliver IPTV streaming over 802.16 wireless systems and propose a simple but effective IPTV channel switching algorithm to keep the channel zapping time in the tolerable range. Besides, we discuss how to allocate channels in the limited bandwidth over wireless network, such as 802.16. The proposed algorithm based on Hot-View channel and personal favorite channel preloading to reduce the network delay and achieve the goal of fast channel switching. Finally, the experimental results show the performance of the proposed algorithm.