FPGA的技术市场特点

本文通过对FPGA行业主要的市场调查公司、供应商和第三方合作伙伴的访问,力图揭示FPGA近期的市场特点及发展态势。     

Semiconducting properties of the anodic films grown over PbIn alloy electrodes

Semiconducting properties of oxide film grown over PbIn binary alloy electrodes in alkaline medium is elucidated. It has been observed that anodic film formed over Pb0.3 wt%In alloy in the potential range of −0.75 to 1.25 V (vs. Hg/HgO) shows better short circuit photocurrent density (3.2 mA/cm²) than the oxide film over pure Pb electrode (2.2 mA/cm²). The observed photoactivity is correlated with the interfacial parameters of the nonstoichiometric oxide–electrolyte interface using Gartner Butler model. Effect of varying concentration of In dopant in the PbO matrix on the interfacial properties is reported. It is shown that combined effect of space charge width and diffusion length of the minority carriers can describe the observed photoactivity efficiently.     

Investigation of semiconducting parameters of Pb---Sn alloy oxide-electrolyte interface by Butler Gartner model

The effect of Sn doping on the photoactivity of anodized Pb---Sn alloy electrodes is discussed. The Butler-Gartner model has been applied to understand the reasons for getting the highest photocurrent with pure lead oxide and the next highest photocurrent with a Pb---Sn alloy oxide containing 0.1 wt.% Sn. The low photoresponse of Sn doped lead oxide is attributed to creation of a small space charge width (5600Å) compared to the large penetration depth (97,000Å) of the incident light.     

Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence

The goal of this study is to show emerging applications of deep learning technology in cancer imaging. Deep learning technology is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Applications of deep learning technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase their survival. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that the sharp increase of the studies of deep learning technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep learning technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new technology, in accordance with Amara's law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new technology can also generate socioeconomic benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare sector among different regions.     

Investigation of semiconducting parameters of PbSn alloy oxide-electrolyte interface by Butler Gartner model

The effect of Sn doping on the photoactivity of anodized PbSn alloy electrodes is discussed. The Butler-Gartner model has been applied to understand the reasons for getting the highest photocurrent with pure lead oxide and the next highest photocurrent with a PbSn alloy oxide containing 0.1 wt.% Sn. The low photoresponse of Sn doped lead oxide is attributed to creation of a small space charge width (5600Å) compared to the large penetration depth (97,000Å) of the incident light.