LED receiver impedance and its effects on LED-LED visible light communications

This paper experimentally demonstrates that the AC impedance spectrum of the LED as a photodetector heavily depends on the received optical power, which may cause the impedance mismatch between the LED and the post trans-impedance amplifier. The optical power dependent impedance of the LED is well fitted by a modified dispersive carrier transport model for inorganic semiconductors. The bandwidth of the LED-LED visible light communication link is further shown to decrease with the optical power received by the LED. This leads to a trade-off between link bandwidth and SNR, and consequently affects the choice of the proper dada modulation scheme.Comment: 9 pages, 9 figures, submitted to Optics Expres

Phototransistor-like Light Controllable IoT Sensor based on Series-connected RGB LEDs

An IoT optical sensor based on the series-connected RGB LEDs is designed, which exhibits the light-controllable optical-to-electrical response like a phototransistor. The IoT sensor has the maximal AC and DC responsivities to the violet light mixed by blue and red light. Its responsivity to the blue light is programmable by the impinging red or green light. A theoretical model based on the light-dependent impedance is developed to interpret its novel optoelectronic response. Such IoT sensor can simultaneously serve as the transmitter and the receiver in the IoT optical communication network, thus significantly reduces the system complexity.Comment: 4 pages, 2 figures, submitted to Electronic Device Letter

Intra-Storm Temporal Patterns of Rainfall in China Using Huff Curves

Intra-storm temporal distributions of precipitation are important for infiltration, runoff, and erosion process understanding and models. A convenient and established method for characterizing precipitation hyetographs is the use of non-dimensional Huff curves. In this study, 11,801 erosive rainfall events with 1 min resolution data collected over 30 to 40 years from 18 weather stations located across the central and eastern parts of China were analyzed to produce Huff curves. Each event was classified according to the quartile period within the event that contained the greatest fraction of rainfall. The results showed that 38.3% of events had the maximum rainfall amounts in the first quartile, followed by the second (26.8%), third (22.4%), and fourth (12.5%) quartiles. Quartile I and II events were generally characteristic of shorter duration and heavier intensity events. Quartile I events averaged 23% shorter durations than quartile IV events, whereas the mean intensity (Iavg), mean maximum 30 min intensity (I30), and mean rainfall erosivity index (EI30) were 1.71, 1.22, and 1.23 times greater, respectively, than those for quartile IV and were significant at a 5% level based on two-sample t-tests. The proportion of quartile I events was less for events of longer duration, whereas the proportions of quartile III and IV events were greater. Two-sample Kolmogorov-Smirnov tests suggested that regional Huff curves can be derived for the central and eastern parts of China. Regional Huff curves developed in this study exhibited dissimilarities in terms of the percentages of storms for different quartiles and the shapes of the curves compared to those reported for Illinois, peninsular Malaysia, and Santa Catarina in Brazil

DeepSeq: Deep Sequential Circuit Learning

Circuit representation learning is a promising research direction in the electronic design automation (EDA) field. With sufficient data for pre-training, the learned general yet effective representation can help to solve multiple downstream EDA tasks by fine-tuning it on a small set of task-related data. However, existing solutions only target combinational circuits, significantly limiting their applications. In this work, we propose DeepSeq, a novel representation learning framework for sequential netlists. Specifically, we introduce a dedicated graph neural network (GNN) with a customized propagation scheme to exploit the temporal correlations between gates in sequential circuits. To ensure effective learning, we propose to use a multi-task training objective with two sets of strongly related supervision: logic probability and transition probability at each node. A novel dual attention aggregation mechanism is introduced to facilitate learning both tasks efficiently. Experimental results on various benchmark circuits show that DeepSeq outperforms other GNN models for sequential circuit learning. We evaluate the generalization capability of DeepSeq on a downstream power estimation task. After fine-tuning, DeepSeq can accurately estimate power across various circuits under different workloads

A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States

Dynamics of the urban extent at fine spatial and temporal resolutions over large areas are crucial for developing urban growth models and achieving sustainable development goals. However, there are limited practices of mapping urban dynamics with these two merits combined. In this study, we proposed a new method to map urban dynamics from Landsat time series data using the Google Earth Engine (GEE) platform and developed a national dataset of annual urban extent (1985–2015) at a fine spatial resolution (30 m) in the conterminous United States (US). First, we derived the change information of urbanized years in four periods that were determined from the National Land Cover Database (NLCD), using a temporal segmentation approach. Then, we classified urban extents in the beginning (1985) and ending (2015) years at the cluster level through the implementation of a change vector analysis (CVA)-based approach. We also developed a hierarchical strategy to apply the CVA-based approach due to the spatially explicit urban sprawl over large areas. The overall accuracy of mapped urbanized years is around 90 % with the 1-year tolerance strategy. The mapped urbanized areas in the beginning and ending years are reliable, with overall accuracies of 96 % and 88 %, respectively. Our results reveal that the total urban area increased by about 20 % during the period of 1985–2015 in the US, and the annual urban area growth is not linear over the years. Overall, the growth pattern of urban extent in most coastal states is plateaued over the past three decades while the states in the Midwestern US show an accelerated growth pattern. The derived annual urban extents are of great use for relevant urban studies such as urban area projection and urban sprawl modeling over large areas. Moreover, the proposed mapping framework is transferable for developing annual dynamics of urban extent in other regions and even globally. The data are available at https://doi.org/10.6084/m9.figshare.8190920.v2 (Li et al., 2019c)