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     

红外弱小目标的匹配方法研究与仿真

研究红外图像中的目标匹配问题。针对传统的红外图像中目标匹配效果不理想,匹配不准确。当红外图像中是弱小目标时,背景灰度值与目标灰度值非常相近,灰度直方图均衡化无法实现目标与背景的完整分离,导致漏匹配率较高的问题。为提高目标的匹配精度,提出一种形态学预处理的红外目标匹配方法,通过形态学算法提取出图像中的形状信息,利用形状信息将红外图像中的目标与背景完整分离,避免了只依靠灰度图像信息造成的不能完整分离目标与背景带来的漏匹配问题。进行仿真的结果证明,采用的形态学与处理的匹配方法能够完整将目标与背景分离,成功完成目标匹配,取得了满意的结果。     

网络信息系统技术安全与防范

信息安全防范是进行全面的安全漏洞检测和分析,依据其结果制定防范措施和解决方案;正确配置防火墙、网络防病毒软件、入侵检测系统、建立安全认证系统等安全系统;完善安全管理规范和机制,切实落实安全管理制度。以增强安全防范意识,确保网络信息系统安全运转。