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Xiaohan Wang(Xiaohan Wang),Yu-Mi Choi(Yu-Mi Choi),Sukyeung Lee(Sukyeung Lee),Myoung-Jae Shin(Myoung-Jae Shin),Jung Yoon Yi(Jung Yoon Yi),Kebede Taye Desta(Kebede Taye Desta ),Hyemyeong Yoon(Hyemyeong 한국자원식물학회 2022 한국자원식물학회지 Vol.35 No.6
Traditional germplasms are unsuitable for mechanized production, limiting adzuki bean production. The creation of cultivars that can be harvested by mechanized means is an urgent task for breeders. The bottom pod height (BPH), lodging resistance, and synchronous maturing of adzuki beans are critical factors for the reduction of losses due to mechanized harvesting. In this study, 14 traits of 806 adzuki bean accessions were analyzed. All growth stages and the yield, lodging score, and synchronous maturing correlated negatively with the BPH. These negative correlations reflect the increased difficulty of breeding to simultaneously satisfy the needs for no lodging, high synchronous maturing rates, BPHs > 10 ㎝, and high yield. We screened three germplasms with no lodging, high synchronous maturing rates, and BPHs > 10 ㎝ that were used as mechanization-adapted breeding material for crossing with high-yield cultivars. Agronomic trait diversity in adzuki beans was also examined in this study. Principal component and cluster analyses were conducted for 806 germplasms resulting in three clusters with the yield and three growth stage traits serving as the main discriminating factors. Cluster 1 included high-yield germplasms with the number of pods per plant and the number of seeds per pod being the major discriminant factors. Cluster 2 included germplasms with long growth periods and large 100-seed weights while cluster 3 contained germplasms with high BPHs. In general, the characteristics that make mechanical harvesting feasible and those assessed in this study could be utilized to choose and enhance adzuki beans production.
Energy-Efficient Collection of Sparse Data in Wireless Sensor Networks Using Sparse Random Matrices
Yu, Xiaohan,Baek, Seung Jun Association for Computing Machinery 2017 ACM transactions on sensor networks Vol.13 No.3
<P>We consider the energy efficiency of collecting sparse data in wireless sensor networks using compressive sensing (CS). We use a sparse random matrix as the sensing matrix, which we call Sparse Random Sampling (SRS). In SRS, only a randomly selected subset of nodes, called the source nodes, are required to report data to the sink. Given the source nodes, we intend to construct a data gathering tree such that (1) it is rooted at the sink and spans every source node and (2) the minimum residual energy of the tree nodes after the data collection is maximized. We first show that this problem is NP-complete and then develop a polynomial time algorithm to approximately solve the problem. We greedily construct a sequence of data gathering trees over multiple rounds and propose a polynomial-time algorithm to collect linearly combined measurements at each round. We show that the proposed algorithm is provably near-optimal. Simulation and experimental results show that the proposed algorithm excels not only in increasing the minimum residual energY, but also in extending the network lifetime.</P>