The size effects on both the photoconductivity and dark conductivity have been observed in m-axial GaN nanowires grown by chemical vapor deposition (CVD). For these nanowires with diameters at 50–130 nm, the products of carrier lifetime (τ) and mobility (μ) derived from the photocurrent measurements are typically at (2–8)×10−1 cm2/V, which are over two orders of magnitude higher than the maximal reported values [τμ=(1–5)×10−4 cm2/V] for their thin film counterparts. A significant decrease of τμ value at diameter below the critical values (dcrt) at 30–40 nm is observed. Similar size dependence is also found from the dark conductivity study. The temperature-dependent measurements further indicate two different thermal activation mechanisms in GaN nanowires with sizes above and below the dcrt. These results suggest a surface-dominant transport property in GaN nanowires both in dark and under light illumination due to the presence of surface depletion and band bending. Probable reasons leading to the smaller dcrt of the CVD-grown m-axial GaN nanowires, compared to the c-axial ones grown by molecular beam epitaxy are discussed as well.

The size-dependent persistent photocurrent (PPC), which refers to a photocurrent persisting for a long time after the excitation light source is terminated, has been investigated in m-axial GaN nanowires (NWs) with diameters of 20-130 nm. These NWs possess polar side surfaces and thus exhibit strong surface band bending (SBB). With different diameters, a different rise time in photoconductivity (PC) upon excitation light illumination and a different decay time in the PPC are observed; the latter is attributed to a long carrier lifetime caused by a frustrated recombination process. The intensity (I)-dependent photocurrent-gain () measurement displays a-I dependence that follows a power-law relationship with fitting indices of ∼0.85-0.89, indicating that the long carrier lifetime-induced PPC of GaN NWs is caused by an SBB effect instead of a bulk trap effect. In addition, size-dependent decay times reveal two regimes for the different sizes of NWs. The decay time of the NW above critical diameter (d crt , 30-40 nm) is found to be ∼13 000 s, while the smaller NW (

No abstract

Extracting useful features from a scene is an essential step in any computer vision and multimedia data analysis task. Though progress has been made in past decades, it is still quite difficult for computers to comprehensively and accurately recognize an object or pinpoint the more complicated semantics of an image or a video. Thus, feature extraction is expected to remain an active research area in advancing computer vision and multimedia data analysis for the foreseeable future.The approaches in feature extraction can be divided into two categories: model-centric and datadriven. The model-centric approach relies on human heuristics to develop a computer model (or algorithm) to extract features from an image. (We use imagery data as our example throughout this chapter.) Some widely used models are Gabor filter, wavelets, and SIFT [42]. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design, such as different lighting conditions and unexpected environmental factors, can render the engineered features less effective. Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data [10]. Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning [27,29].Both model-centric and data-driven approaches employ a model (algorithm or machine). The differences between model-centric and data-driven can be told in two related aspects:• Can data affect model parameters? With model-centric, training data does not affect the model. With data-driven, such as MLP or CNN, their internal parameters are changed/learned based on the discovered structure in large data sets [38].• Can data affect representations? Whereas more data can help a data-driven approach to improve representations, more data cannot change the features extracted by a model-centric approach. For example, the features of an image can be affected by the other images in CNN (because the structure parameters modified through backpropagation are affected by all training images). But the feature set of an image is invariant of the other images in a model-centric pipeline such as SIFT.The greater the quantity and diversity of data, the better the representations can be learned by a data-driven pipeline. In other words, if a learning algorithm has seen enough training instances of an object under various conditions, e.g., in different postures and has been partially occluded, then the features learned from the training data will be more comprehensive. The focus of this chapter is on how neural network, specifically convolutional neural network (CNN), achieves effective representation learning. Neural network, a neuroscience-motivated model, was based on Hubel and Wiesel's research on cats' visual corte...

No abstract