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

Rotor fault classification technique and precision analysis with kernel principal component analysis and multi-support vector machines

To solve the diagnosis problem of fault classification for aero-engine vibration over standard during test, a fault diagnosis classification approach based on kernel principal component analysis (KPCA) feature extraction and multi-support vector machines (SVM) is proposed, which extracted the feature of testing cell standard fault samples through exhausting the capability of nonlinear feature extraction of KPCA. By computing inner product kernel functions of original feature space, the vibration signal of rotor is transformed from principal low dimensional feature space to high dimensional feature spaces by this nonlinear map. Then, the nonlinear principal components of original low dimensional space are obtained by performing PCA on the high dimensional feature spaces. During muti-SVM training period, as eigenvectors, the nonlinear principal components are separated into training set and test set, and penalty parameter and kernel function parameter are optimized by adopting genetic optimization algorithm. A high classification accuracy of training set and test set is sustained and over-fitting and under-fitting are avoided. Experiment results indicate that this method has good performance in distinguishing different aero-engine fault mode, and is suitable for fault recognition of a high speed rotor

Magnetic helicity evolution during active region emergence and subsequent flare productivity

Aims. Solar active regions (ARs), which are formed by flux emergence, serve as the primary sources of solar eruptions. However, the specific physical mechanism that governs the emergence process and its relationship with flare productivity remains to be thoroughly understood. Methods. We examined 136 emerging ARs, focusing on the evolution of their magnetic helicity and magnetic energy during the emergence phase. Based on the relation between helicity accumulation and magnetic flux evolution, we categorized the samples and investigated their flare productivity. Results. The emerging ARs we studied can be categorized into three types, Type-I, Type-II, and Type-III, and they account for 52.2%, 25%, and 22.8% of the total number in our sample, respectively. Type-I ARs exhibit a synchronous increase in both the magnetic flux and magnetic helicity, while the magnetic helicity in Type-II ARs displays a lag in increasing behind the magnetic flux. Type-III ARs show obvious helicity injections of opposite signs. Significantly, 90% of the flare-productive ARs (flare index > 6) were identified as Type-I ARs, suggesting that this type of AR has a higher potential to become flare productive. In contrast, Type-II and Type-III ARs exhibited a low and moderate likelihood of becoming active, respectively. Our statistical analysis also revealed that Type-I ARs accumulate more magnetic helicity and energy, far beyond what is found in Type-II and Type-III ARs. Moreover, we observed that flare-productive ARs consistently accumulate a significant amount of helicity and energy during their emergence phase. Conclusions. These findings provide valuable insight into the flux emergence phenomena, offering promising possibilities for early-stage predictions of solar eruptions

KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.Comment: accepted at NAACL 2022 main conferenc