无线鼠标电路设计及算法实现

文中设计的无线鼠标包含发射电路和接收电路两部分,发射电路先采用光传感器ADNS5030检测鼠标的移动信息,然后输出到内置单片机的无线模块CC2430的I/O口,通过CC2430控制并将此信息发射到接收电路,接收电路接收到移动信息后,根据鼠标位移状态机解码算法,得到移动信息及按键信息,通过HT82M98A处理后将信息传给计算机,计算机自动完成相应动作;然后不断的循环;经测试,该电路不仅具备普通有线鼠标的全部功能,还可远距离灵活操纵鼠标,在大礼堂等开阔场所可达80 m以上;计算机自动识别无线鼠标接收电路,不需要额外安装程序,每一次鼠标信号处理总时间不大于1 ms,无线鼠标移动灵活,无停滞现象.     

一种基于特征点匹配的图像拼接方法研究

针对图像间因具有旋转及光线强度差异等现象而导致的拼接效果不佳及拼接速度慢的问题,提出一种基于特征点的配准算法;在特征点提取阶段,尺度不变的特征变换方法 (SIFT)具有对图像尺度缩放、旋转、放射变换以及亮度变化保持不变的优点,文章采用了改进的SIFT特征点提取算法;在特征点匹配阶段,采用改进的RANSAC算法对特征点匹配对提纯;最后用加权平均法实现拼接图像的融合;实验证明,该算法有效提高了图像拼接的效率和准确性,拼接精度可以达到亚像素级。     

Influence of base resistance on extracting thermal resistance for SiGe HBTs

In this article, the influence of base resistance on extracting thermal resistance for SiGe heterojunction bipolar transistors is studied and an improved approach for determining the junction temperature and thermal resistance is presented. The proposed method for extracting thermal resistance is based on the temperature sensitivity of the base–emitter (B–E) voltage when the device is biased with a fixed emitter current density. This approach not only takes into account the self‐heating during the different ambient temperature measurement but also revises the empirical equation of B–E voltage due to the influence of base resistance during the power dissipation increment measurement. Results are obtained for devices with different emitter lengths and fingers. Compared with the conventional method, the thermal resistance is about up to 15% improvement for the device with 0.3 × 1.9 μm² emitter area and 13.8% for the device with 0.3 × 13.9 μm² emitter area. The accurate thermal resistance implemented in HICUM model has resulted in better fit for transistor output characteristics. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Probability-confidence-kernel-based localized multiple kernel learning with lp norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.     

Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

A Polynomial Algorithm for Computing Elementary Siphons in a Class of Petri Nets

A set of elementary siphons plays a key role in the development of deadlock prevention policies for automated manufacturing systems. This paper addresses the computation problem for elementary siphons in a subclass of Petri nets which are basic systems of simple sequential processes with resources (BS³PR) and can model many automated manufacturing systems. An algorithm for enumerating elementary siphons is established by the one‐to‐one relationship between maximal perfect resource‐transition circuits (MPCs) and strict minimal siphons. A set of MPCs is first computed, followed by a set of elementary siphons in a BS³PR. The presented algorithm is proved to have polynomial‐time complexity. An example is used to illustrate the algorithm.     

Intelligent control of doubly‐fed induction generator systems using PIDNNs

An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a proportional‐integral‐derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand‐alone power supply system or as the emergency power system when the electricity grid fails for all sub‐synchronous, synchronous, and super‐synchronous conditions. The rotor side converter is controlled using field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field‐oriented control, is primarily implemented to maintain the magnitude of the DC‐link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

New constructions of OSBE schemes and their applications in oblivious access control

Oblivious signature-based envelope (OSBE) schemes have demonstrated their potential applications in the protection of users privacy and rights. In an OSBE protocol, an encrypted message can only be decrypted by the receiver who holds a valid signature on a public message, while the sender (encrypter) does not know whether the receiver has the signature or not. Our major contributions in this work lie in the following aspects. We improve the notion of OSBE so that a valid credential holder cannot share his/her credential with other users (i.e., all-or-nothing non-transferability). We clarify the relationship between one-round OSBE and identity-based encryption (IBE) and show that one-round OSBE and semantically secure IBE against the adaptively chosen identity attack (IND-ID-CPA) are equivalent, if the signature in the OSBE scheme is existentially unforgeable against adaptively chosen message attacks. We propose an oblivious access control scheme to protect user privacy without the aid of any zero-knowledge proof. Finally, we also highlight some other novel applications of OSBE, such as attributed-based encryption.     

The calcium-phosphorus and the simultaneous calcium-oxygen and calcium-sulfur equilibria in liquid iron

The equilibrium Ca₃P_2(s) = 3[Ca] + 2[P] was studied at 1600 ° by equilibrating liquid iron, saturated with Ca₃P₂, and contained in a TiN crucible, with Ca vapor. The source of Ca was liquid Ca contained in an Mo crucible, and the vapor pressure of Ca was varied by varying the position of the Mo crucible in the temperature gradient of a vertical tube furnace. A least-squares analysis of the data gave and. The simultaneous equilibria CaO_(s) = [Ca] + [O] and CaS_(s) = [Ca] + [S] were studied at 1600 ° by equilibrating liquid iron, contained in a pressed and sintered CaO-CaS crucible with Ca vapor. The advantage of this technique is that two equilibrium constants,K _cas andK _cao, and two interaction coefficients, and can be determined from one set of experiments. It was determined that, at 1600 °,K _cas = 5.9 × 10⁻⁸ K _cao = 5.5 × 10⁻⁹,, and. Formerly Graduate Students