Indoor hydroponics system is gaining acceptance and traction in providing practical indoor farming capabilities for urban dwellers, including in low income housing estates. However, for the low income urban dwellers, the size of their residence might restrict the design of the indoor hydroponics system, especially in terms of available floor space. Furthermore, before one starts to develop an indoor hydroponics system, it is imperative to identify users’ preferences, in terms of the types of plants to grow, price, and design to enable the researchers to develop a better indoor hydroponics system. In this study, opinions from 53 participants on indoor hydroponics systems were obtained and analysed. Four aspects were analysed via the survey: (1) customer evaluations; (2) positive value; (3) costing; and (4) purchasing proclivity. The study shows that participants prefer to grow edible plants because of their benefits. Participants also prefer systems priced at below RM100 (60.40% of the respondent). Aloe Vera (56.6% preference) and Brazilian Spinach(49.1% preference) are two types of plants most preferred by the participants. As mentioned previously, the output of this survey will be used to guide the process of developing a suitable indoor hydroponics system for the low-income urban dwellers.
Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots.144 frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-theart algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems.
There has been a number of researches carried out on Human-Computer Interaction (HCI) impact to home networking. Many researchers have stated that the HCI elements are the most important aspects to be considered in making user understand some of issues concerning the Home Network. This paper reviews the existing research related to Human-Computer Interaction, Home Network and Network Management. This paper seeks to identify the effectiveness of existing Network Management Tools and the importance of HCI in dealing with it. In addition, this paper looks into the potential future work that could be done in order to archive desirable goals of Home Network.
Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.