Bis{μ-1,3-bis­[(2-methyl-1H-benzimid­azol-1-yl)meth­yl]benzene-κ2 N 3:N 3′}bis­(diiodidocadmium)

In the title compound, [Cd2I4(C24H22N4)2], the 1,3-bis­[(2-methyl-1H-benzimidazol-1-yl)meth­yl]benzene ligand bridges two CdI2 units, forming a centrosymmetric dinuclear complex. The CdII atom adopts a distorted tetra­hedral coordination geometry. In the crystal, complex mol­ecules are linked into columns parallel to [101] by π–π stacking inter­actions, with centroid–centroid distances of 3.558 (2) Å

Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context

Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection

Design and implementation of real time image acquisition and processing systems

Nondestructive evaluation (NDE) is a way to characterize a material or a structure without compromising its usability. Generally, the inspection methods of NDE testing may be based on acoustics, penetrating radiation, light, electric and magnetic fields, or more special possibilities. Whatever methods are used in NDE, imaging technology is one of the important components for these systems. The rapid growth of sophisticated and low priced image acquisition and processing devices has opened up the possibility of applying imaging analysis to more NDE areas. Imaging technology is becoming a very powerful tool in NDE for material properties. The objective of this thesis is to develop a robust, open, easily extendable software platform for real time imaging acquisition and processing. This platform can support image format transform, histogram based look up table, real time image/slice display and device control integration. Three applications were implemented based on this platform. For Rapid Whole-Kernel Single-Seed Analyzer project, the special requirements for the CCD camera and Liquid Crystal Tunable Filter (LCTF) control were met. Multi-thread synchronization was used to cooperate between the CCD camera and the LCTF control. In order to speed up the whole image acquisition procedure, a predefined palette was used. The overlapping between the LCTF tuning time and image storing time made the whole data acquisition as fast as possible. This thesis also used the 14 bit cooled CCD camera to do radiographic digitization. Calibration, focusing, and distance measurement were implemented. The test showed the system could meet the basic requirements for radiographic digitization. In new X-ray Vision system, real time image/slice displaying under multi-video systems were developed. Image integration, averaging and subtracting were finished. It also provided a friendly user interface to motion control. Based on the integration of image acquisition and motion control, the automation of real-time scans was achieved. It is very flexible and can be used in complicated automatic scanning. The tests for the above three applications showed this platform has high stability and powerful functionality