蓝牙的服务发现协议（SDP）的研究

蓝牙服务发现协议（SDP）是蓝牙系统的核心协议,也是蓝牙系统所有用户模式的基础。本文首先简要介绍了Bluetooth的基本概念,然后对蓝牙服务发现协议的概貌进行了分析,并对SDP的研究方法提出了意见。     

基于pSOS下蓝牙核心协议的开发与典型应用的设计及实现

根据蓝牙特别利益小组规定的开发标准，在pSOS操作系统下，开发了与蓝牙模块通信的主控制器接口，以及蓝牙核心协议的L2CAP协议、RFCOMM协议和SDP协议，设计并实现了一个典型的应用一文件传输模型，给出了文件传输过程的流程．测试结果表明，该系统具有良好的可靠性和可用性，支持蓝牙产品的开发．     

基于嵌入式Linux的蓝牙以太网关设计

蓝牙技术作为一种新型短程无线通信技术,存在与其他通信协议的兼容性问题,介绍了蓝牙基本组网机制和数据分组格式,深入分析了蓝牙业务发现协议SDP和蓝牙局域网接入规范LAP,提出本地接入点LAP和蓝牙终端的一般业务发现过程.然后,基于嵌入式Linux操作系统搭建蓝牙网关嵌入式开发平台,提出可行的蓝牙网关系统结构,实现蓝牙匹克网与有线以太网之间的互联互通.     

蓝牙-家庭网络中服务调用的设计和实现

家庭网络是一种新兴的网络。面向普通家庭的特点决定了家庭网络对于服务调用的实现接口要求更简单更方便。蓝牙技术凭借良好的技术特点在家庭网络中有着广阔的应用前景。但是SIG规范并未给出如何实现具体的服务调用。本文分析了现有的服务调用方式。探讨了家庭网络的结构特点，并由此设计了针对家庭网络的服务调用模型：以XML为核心．SOAP和蓝牙SDP模块共用的方式，并在我们设计的家庭网络系统中进行了实现。     

基于蓝牙技术的家庭网络系统的设计和实现

在以前的项目中 ,实现了一个具有家庭网关等功能的家庭网络平台 ( HNP) .为了使用户随时随地的使用家庭中的服务 ,围绕着 HNP设计了一个解决方案 -使用蓝牙技术搭建无线家庭网络系统 .SIG规范中的蓝牙服务发现协议是针对运动环境的 ,对服务调用的实现也未做出规定 .针对家庭网络的特点 ,在本解决方案中扩充了蓝牙服务发现协议 ,使其具有服务注册、服务声明等功能 ,并尝试了一种新的服务调用机制 :以 XML为核心 ,SOAP和蓝牙 SDP模块共用的方式来实现服务调用     

蓝牙打印机应用模型的实现与互连测试

蓝牙是一种采用无线电波传输代替移动设备之间电缆的短距离无线通信技术。蓝牙打印机应用模型是蓝牙技术在计算机与打印机之间的电缆替代应用。简要介绍了蓝牙技术的概况以及蓝牙打印机应用模型，重点介绍了实现蓝牙打印机的方法及其互连测试的方案。     

基于蓝牙技术的环绕智能感知系统

提出了将蓝牙技术应用到环绕智能感知系统中，使系统能够快速感知用户的进入、离开和身份。制定了在环绕智能中的蓝牙感知策略，设计了蓝牙感知器和蓝牙适配器的软、硬件体系结构、通信规范，给出了实现的关键技术；解决了适配器与主机的接口通信问题；利用本方案实现了蓝牙感知器、蓝牙适配器，并应用到环绕智能中，实验结果表明系统运行稳定，感知速度快。     

蓝牙协议及其在Headset中的应用

介绍了蓝牙协议栈体系结构以及蓝牙耳机的基本原理和应用模型，并且详细阐述该蓝牙耳机系统的开发方法，最后具体给出基于CSR蓝牙单芯片的嵌入式蓝牙系统硬件结构和软件结构，在此框架下，可快速方便开发嵌入式蓝牙产品。     

基于蓝牙技术的体育比赛打分系统

蓝牙是一种短距离无线通信技术。本文从蓝牙协议栈中的HCI层入手，对该层的主要功能和工作机制进行了剖析，进而阐述了基于HCI层的蓝牙应用开发方案，并研究设计了一个基于蓝牙技术的产品实例——蓝牙无线打分系统。     

蓝牙硬件电路

蓝牙硬件电路是实现蓝牙技术的基础。全书分7章时蓝牙硬件电路进行了全面和详细的介绍，内容包括蓝牙技术基础、蓝牙无线电收发器、蓝牙功率放大器、蓝牙基带控制器、蓝牙单片系统(解决方案)、蓝牙开发系统及蓝牙无线电测试等。介绍了30多家公司70多种不同类型的蓝牙芯片，给出了大部分芯片的详细技术性能指标、引脚功能、芯片内部结构、应用电路及元器件参数及芯片封装形式与尺寸。内容新颖，系统全面，工程性好，实用性强。可作为从事无线通信、移动通信、     

移动Ad hoc网络服务发现协议

介绍移动Ad hoc网络的应用前景和主要研究内容,阐述服务协议的重要性以及服务发现协议中的基本概念,针对现有的移动Ad hoc网络服务发现协议的核心技术和设计思想进行分析,选取几种典型的服务发现协议进行对比,总结得出各类服务发现协议的优缺点和适用范围,并指出该领域的进一步研究方向。     

SDP协议在视频点播系统中的应用

会话描述协议是一个重要的支持流媒体的应用层网络协议，而视频点播系统是当前Internet上的一个重要的多媒体应用。本文介绍了会话描述协议在网络视频点播系统中的应用及具体实现。     

Systematic decision process for intelligent decision making

In this paper, Systematic decision process (SDP) for solving Multiple Criteria Decision Making problems with application for manufacturing location selection is introduced. SDP is a comprehensive approach which is based on eliciting strength of preferences for assessing additive utility functions. SDP consists of three steps: I. assessing weights, II. assessing qualitative criteria, and III. ranking alternatives using the assessed additive utility function. Strengths of preferences can be expressed by using either qualitative or numerical ratings. If the decision maker is inconsistent in his/her responses, such inconsistencies are identified by the method. It is shown that the method has advantages in terms of simplicity and accuracy compared to existing methods such as Analytical Hierarchy Process. Furthermore, a quadratic optimization method for assessing weights of additive utility function by use of pair comparison of actual alternatives is developed. Computational experiments are provided.     

An efficient multiple-kernel learning for pattern classification

Support vector machines (SVMs) have been broadly applied to classification problems. However, a successful application of SVMs depends heavily on the determination of the right type and suitable hyperparameter settings of kernel functions. Recently, multiple-kernel learning (MKL) algorithms have been developed to deal with these issues by combining different kernels together. The weight with each kernel in the combination is obtained through learning. Lanckriet et al. proposed a way of deriving the weights by transforming the learning into a semidefinite programming (SDP) problem with a transduction setting. However, the amount of time and space required by this method is demanding. In this paper, we reformulate the SDP problem with an induction setting and incorporate two strategies to reduce the search complexity of the learning process, based on the comments discussed in the Lanckriet et al. paper. The primal and dual forms of SDP are derived. A discussion on computation complexity is given. Experimental results obtained from synthetic and benchmark datasets show that the proposed method runs efficiently in multiple-kernel learning.     

服务发现协议的设计和实现—应用于蓝牙—家庭网络中

服务发现协议是网络自配置、实现互操作的核心协议,现在正成为研究热点。家庭网络是一种新兴的网络,面向普通家庭的特点决定了家庭网络对于服务发现协议的功能要求更高。蓝牙技术凭借良好的技术特点在家庭网络中有着广阔的应用前景。该文分析了5种技术领先的服务发现协议,探讨了家庭网络的结构特点,设计了针对家庭网络的服务发现协议。并在蓝牙服务发现协议的基础上,添加了服务注册、服务声明等功能模块,最后在所设计的家庭网络系统中进行了实现。     

Efficient semidefinite solutions for TDOA-based source localization under unknown PS

Two efficient solutions via Semi-Definite Programming (SDP) are proposed for source localization problems using time difference of arrival (TDOA)-based ranging measurements when the propagation speed (PS) is unavailable and considered as a variable. For this problem, we propose a relaxed SDP (RSDP) solution, the performance of which is suboptimal. Accordingly, we propose a two-stage SDP method to improve the performance by applying the rank-reduction method. Besides, we also propose a penalty function-based SDP (PF-SDP) by introducing the penalty term. By doing so, the cost function becomes tighter so that the solution performs better. The simulated results show that the performance of two-stage SDP is sufficiently close to the Cramér-Rao Lower Bound (CRLB) accuracy at high noise levels. The PF-SDP outperforms the two-stage SDP in the presence of low noise levels.     

无线传感器网络中的线性移动目标运动参数捕获方法

移动目标跟踪即移动目标的运动路径与参数获取在无线传感器网络应用中具有重要的研究价值.采用移动目标节点与信标节点间的TOA测量方法,提出了无线传感器网络中移动目标运动参数的捕获方法.通过建立移动目标运动参数的估计模型,本文首先推导了线性移动目标初始位置及移动速度估计的非约束线性最小二乘(ULLS)和约束线性最小二乘(CLLS)方法.将估计模型松弛为凸优化的半正定规划(SDP)问题,又设计了运动参数捕获的SDP算法.仿真分析结果表明,在3种所设计算法中ULLS算法的估计误差最大,SDP算法其次,CLLS算法的估计误差最小.随着采样周期的增加,初始位置的估计误差亦稍有增大,但速度估计误差却在减少.更多的采样点数量有利于增加测量信息量,可以有效减少位置及速度估计误差.     

Convex optimization algorithms for cooperative RSS-based sensor localization

To obtain a global solution for the source location estimates, the cost function of RSS-based sensor localization is relaxed as convex optimization problem which can be solved by interior point method. Weighted squared least square (WSLS) and weighted least square (WLS) based optimization functions are proposed to locate the source nodes. The corresponding semidefinite programming (SDP), second-order cone program (SOCP) and mixed SOC/SDP algorithms are designed by considering the known or unknown transmit powers. The computational complexity of the proposed algorithms is derived by analyzing the number of variables and equality constraints produced in the relaxation. The simulations show that the mixed SOC/SDP runs faster than the SDP, although the algorithms have the approximately equal accuracy performance. Whether the transmit power is known or not, the accuracy performance of the WLS-SDP is better than that of the WSLS-SDP and WSLS-SOC/SDP algorithms. However the computational complexity of the WLS-SDP is greatly larger than that of WSLS-SOC/SDP and WSLS-SDP due to a large number of variables.     

Neuro-dynamic programming for designing water reservoir network management policies

Stochastic dynamic programming (SDP) can improve the management of a multipurpose water reservoir by generating management policies which are efficient with respect to the management objectives (flood protection, water supply for irrigation, hydropower generation, etc.). The improvement in efficiency is even more remarkable for networks of reservoirs. Unfortunately, SDP is affected by the well-known ‘curse of dimensionality’, i.e. computational time and computer memory occupation increase exponentially with the dimension of the problem (number of reservoirs), and the problem rapidly becomes intractable. Neuro-dynamic programming (NDP) can sensibly mitigate this limitation by approximating Bellman functions with artificial neural networks (ANNs). In this paper the application of NDP to the problem of the management of reservoir networks is introduced. Results obtained in a real-world case study are finally presented.     

State-dependent control of a hydraulically actuated nuclear decommissioning robot

This paper develops and evaluates state-dependent parameter (SDP) control systems for the hydraulically actuated dual-manipulators of a mobile nuclear decommissioning robot. A unified framework for calibration and SDP model identification is proposed, in which the state-dependent variable is a delayed voltage input associated with the time-varying system gain. Such nonlinearities can cause undesirable joint movements under automatic control. Hence, the present paper develops a nonlinear pole assignment algorithm for the SDP model. Closed-loop experimental data shows that the SDP design more closely follows the joint angle commands than an equivalent linear algorithm, offering improved resolved motion.