The chopper cascade speed-adjusting device is a kind of energy saving product of speed performance and cost-effective, which has been widely used in AC speed-adjusting. The main circuit topology is introduced and the working principle is analyzed of the AC motor chopper cascade speed-adjusting in this article. Chopper control makes the system power factor improved. The IGBT is selected as chopper switching device instead of thyristor chopper, which improves the speed performance of the system and overcomes forced commutation shortcomings of the SCR. Also, the line is simple and the control is convenient. The TMS320LF2407A DSP processor of TI is used as the main control chip to realize the digital control of chopper cascade speed-adjusting system, the system's hardware and software diagram are showed, and the experimental result is acquired in this paper. The result shows that the system runs reliably and the desired effect is achieved.

Social network Analysis research has attracted much attention in recent years. Link prediction is a key research direction within this area. Social network analysis (SNA) and supervised learning with classification algorithms are used in this research for the link prediction in social networking. SNA is used to analyze the key factors influencing information diffusion model bout density, centrality and the cohesive subgroup, reveals useful insights which are the relationships and activities between human and identify the influencing nodes. The supervised learning with the classification algorithms of Independent Cascade Model [6] is used to focus on discovering surprising links in the existing ones of influencing nodes. The partitions store discrete characteristics of nodes. The supervised learning for prediction which nodes in a social network will be linked next from the attacked influencing nodes with the classification algorithms to monitoring the risk. Such understanding can lead to efficient implementation of tools to link prediction in social network. They are applied as a guide to further investigation of social network behaviors and improve the security model for social networking to preventing and controlling information diffusion or computer viruses in network application.

It is very urgent to design higher speed FFT processor due to higher requirement in LTE-Advanced system. In this paper, we propose a new parallel cascade architecture for FFT processor. The architecture, which combines advantages of parallel with that of cascade architecture, is flexible in parallel grade. To simplify accessing memories, propose a new address mapping scheme, too. With the property of in-place operation, only a RAM is used to store in-out data. The scheme is very efficient for generating SC-FDMA baseband signal in LTE-Advanced system.

This paper proposes a new real-time side pedestrian detection method for driver assistance systems. The approach includes two steps, classification and verification. In classification step, we use the Haar feature and a cascade of stage classifiers trained by Adaboost algorithm. In verification step, we use HOG feature and SVM classifier to refine false positives. To speed up the processing time, we use OCS algorithm in classification step. Meanwhile, we also propose a new dataset using an onboard camera to shoot side pedestrians on zebra in urban environment. For a 704*576 image, the system can process more than 15FPS. We experimentally found that the detection rate is about 90% while the false alarm rate is less than 1%.

Nowadays the research on Road Safety is receiving more and more attention, and the pedestrian detection is the significant part of such research. Our study focus on the feature of pedestrian's legs because they have an obvious difference from the background when people walking across the road. In paper, we present a novel idea that contributes to low-false alarms and high detection rate. First, we detect the pedestrian based on Haar cascade classifier which is trained by the leg samples. Second, we verify the pedestrian through the HOG descriptors. The results from experiments show our method gets a detection rate of about 87% with few false alarms.

In this paper we address the following question. How much can information complexity be increased via a chaotic encryption and convolutional coding? To address this, the algorithmic complexity of data sequences generated from a cascade of one-stage chaotic cryptosystem and a one-stage convolutionally encoding mechanism is examined. An algorithm that evaluates the efficiency and performance of this mechanism for a secure digital communication is designed. Special attention was paid to the algorithmic complexity of computing information for an entire class of rate-1∕2 encoders. Numerical evidence indicates algorithmic complexity associated with the chaotic-encrypted convolutionally-encoded data increases along with the increase of the error correcting capacity of the decoder.

The theoretical methods to analyze the flow in cascades presented in Chapter 5 are useful design and analysis methods, but they are not sufficient for day-to-day axial-flow compressor design activity. The many limitations restricting the accuracy and range of application of those methods have been discussed in Chapter 5. Consequently, empirical models are commonly used as a means of predicting the basic performance of cascades in axial-flow compressor design and analysis. These empirical models are derived from experimental data obtained from two-dimensional cascade testing. Extensive testing of this type has been accomplished, particularly by the NACA. Rather sophisticated empirical methods are available for the standard blade profiles discussed in Chapter 4. In the case of advanced blade types, such as the controlled diffusion airfoils discussed in Chapter 4, alternate empirical models may be required. Typically these will be an adaptation of the methods used for the standard airfoil profiles, specifically addressing the improved performance characteristics achieved by the new profile design. The basic objective of the empirical modeling process is to predict the fluid turning and total pressure loss for a cascade under fairly general operating conditions. Also, operating conditions where near-optimum performance can be expected need to be identified by the empirical models. These are often referred to as design conditions, since they are appropriate for use under the compressor's design operating condition where optimum performance is usually desired. Indeed, it will be seen in Chapters 10 and 11 that these can be used as a basis for selecting blade geometry to match the desired flow field through the compressor. Special empirical models to address blade tip leakage and seal leakage through stator shroud seals will also be covered in this chapter.