Design, optimization, and performance analysis of new photodiode structures for CMOS active-pixel-sensor (APS) imager applications

The dark current in the active-pixel-sensor (APS) cell of a CMOS imager is known to be mainly generated in the regions of bird's beak after the local oxidation of silicon process as well as the surface damage caused by the implantation of high doping concentration. Furthermore, shallow and deep pn-junctions can improve the photo-sensitivity for light of short and long wavelengths, respectively. In this paper, two new photodiode structures using p-substrate and lightly-doped sensor implant SN- as pn-junction photodiode with the regions of bird's beak embraced by SN- and p-field implants, respectively, are proposed and analyzed to reduce dark current and enhance the overall spectral response. 5 /spl mu/m/spl times/5 /spl mu/m APS cells fabricated in a 0.35-/spl mu/m single-poly-triple-metal (1P3M) 3.3-V CMOS process are designed by using the proposed photodiode structures. As shown from the experimental results, the two proposed photodiode structures of 5 /spl mu/m/spl times/5 /spl mu/m APS cells have lower dark currents of 30.6 mV/s and 35.2 mV/s at the reverse-biased voltage of 2 V and higher spectral response, as compared to the conventional structure and other photodiode structures. Thus, the two proposed new photodiode structures can be applied to CMOS imager systems with small pixel size, high resolution, and high quality.     

Mixed graph convolution and residual transformation network for skeleton-based action recognition

Action recognition based on a human skeleton is an extremely challenging research problem. The temporal information contained in the human skeleton is more difficult to extract than the spatial information. Many researchers focus on graph convolution networks and apply them to action recognition. In this study, an action recognition method based on a two-stream network called RNXt-GCN is proposed on the basis of the Spatial-Temporal Graph Convolutional Network (ST-GCN). The human skeleton is converted first into a spatial-temporal graph and a SkeleMotion image which are input into ST-GCN and ResNeXt, respectively, for performing the spatial-temporal convolution. The convolved features are then fused. The proposed method models the temporal information in action from the amplitude and direction of the action and addresses the shortcomings of isolated temporal information in the ST-GCN. The experiments are comprehensively performed on the four datasets: 1) UTD-MHAD, 2) Northwestern-UCLA, 3) NTU RGB-D 60, and 4) NTU RGB-D 120. The proposed model shows very competitive results compared with other models in our experiments. On the experiments of NTU RGB?+?D 120 dataset, our proposed model outperforms those of the state-of-the-art two-stream models.

     

Knowledge-based image retrieval with spatial and temporalconstructs

A knowledge-based approach to retrieve medical images by feature and content with spatial and temporal constructs is developed. Selected objects of interest in an image are segmented and contours are generated. Features and content are extracted and stored in a database. Knowledge about image features can be expressed as a type abstraction hierarchy (TAH), the high-level nodes of which represent the most general concepts. Traversing TAH nodes allows approximate matching by feature and content if an exact match is not available. TAHs can be generated automatically by clustering algorithms based on feature values in the databases and hence are scalable to large collections of image features. Since TAHs are generated based on user classes and applications, they are context- and user-sensitive. A knowledge-based semantic image model is proposed to represent the various aspects of an image object's characteristics. The model provides a mechanism for accessing and processing spatial, evolutionary and temporal queries. A knowledge-based spatial temporal query language (KSTL) has been developed that extends ODMG's OQL and supports approximate matching of features and content, conceptual terms and temporal logic predicates. Further, a visual query language has been developed that accepts point-click-and-drag visual iconic input on the screen that is then translated into KSTL. User models are introduced to provide default parameter values for specifying query conditions. We have implemented the KMeD (Knowledge-based Medical Database) system using these concepts     

A knowledge-based approach for retrieving images by content

A knowledge based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. Interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain specific image knowledge. A three layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context sensitive. The performance of the proposed knowledge based query processing is also discussed     

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

Microsystem Technologies - Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and...     

Oxidative steam reforming of ethanol for hydrogen production on M/Al2O3

In this work, we investigate oxidative steam reforming (OSR) of ethanol on a series of metals under various catalytic conditions (H₂O/ethanol and O₂/ethanol ratios) to understand the reaction mechanism and to optimize the catalytic conditions for optimal hydrogen production. There are three reaction pathways for OSR using these metals. Ethanol can be oxidized to acetaldehyde on Cu, Ag and Au, and it can be dehydrated to form ethylene on Co, Ni, Pd and Pt. Ethylene can form coke and degrade catalysts after the long-term OSR. In the third pathway, ethanol preferentially breaks its C–C bond and is further oxidized to CO or CO₂ on Ru, Rh and Ir, providing optimal hydrogen production. In addition, increasing H₂O/ethanol and O₂/ethanol ratios can improve catalytic activity, attributable to atomic oxygen from H₂O and O₂ efficiently rupturing the C–C bond of ethanol. This concept explains the improved performance of OSR on the CeO₂-modified catalyst, which shows better oxygen storage capability.     

Improvement of the CFRP Mechanical Properties Using Taguchi Method to Optimize the Formation Conditions

Carbon fiber composites have high strength, high stiffness and light weight characteristics to apply to many fields, such as leisure, energy and transportation industries. The CFRP （carbon fiber reinforced polymers/plastics） composites made of carbon fibers as reinforcement and epoxy resins as matrix were prepared by drum winding process. Various parameters such as molding temperature, molding pressure and pressing time were selected as the pre-pregs were laminated to be the CFRP. The effects of fabricating parameters which affected the mechanical properties of CFRPs were analyzed by Taguchi method in this study. The results showed that molding temperature was the main factor to influence the mechanical properties of composites.