物联网产业是指涉及数据采集、传感器网络组网和协同信息处理、网络通信、智能处理、系统集成、信息安全等各种活动的集合，涵盖传感器芯片、微能源等核心基础产业，传感器、射频识别（RFID）为主的感知制造业，通信网络设备制造、传感器网络设备制造以及机器到机器（M2M）网络设备制造等为主的基础网络制造业，和提供网络传输、信息处理以及运营服务等的应用服务业等。

报告从政策监管、行业要闻、区域要闻、企业要闻几个方面对10月下半月通信行业要闻进行了综述。

公司传统上以电子文档影像业务、电子商务和信息安全三大软件产品创业，随后在金融IT遇到瓶颈时开拓环保领域，并逐步确立了金融IT与环保科技业务并举的战略。公司作为金融软件提供商，以金融行业IT设作为自己的主营业务发展方向，形成软硬件和IT服务综合的银行IT 解决方案提供商。

报告包括2013年11月上半月通信行业的政策监管、行业要闻、区域要闻、企业要闻、海外要闻。

Higher-level representations (macromodels, reduced-order models) abstract away unnecessary implementation details and model only important system properties such as functionality. This methodology -- well-developed for linear systems and digital (Boolean) circuits -- is not mature for general nonlinear systems (such as analog/mixed-signal circuits). Questions arise regarding abstracting/macromodeling nonlinear dynamical systems: What are ``important'' system properties to preserve in the macromodel? What is the appropriate representation of the macromodel? What is the general algorithmic framework to develop a macromodel? How to automatically derive a macromodel from a white-box/black-box model. This dissertation presents techniques for solving the problem of macromodeling nonlinear dynamical systems by trying to answer these questions. We formulate the nonlinear model order reduction problem as an optimization problem and present a general nonlinear projection framework that encompasses previous linear projection-based techniques as well as the techniques developed in this dissertation. We illustrate that nonlinear projection is natural and appropriate for reducing nonlinear systems, and can achieve more compact and accurate reduced models than linear projection. The first method, ManiMOR, is a direct implementation of the nonlinear projection framework. It generates a nonlinear reduced model by projection on a general-purpose nonlinear manifold. The proposed manifold can be proven to capture important system dynamics such as DC and AC responses. We develop numerical methods that alleviates the computational cost of the reduced model which is otherwise too expensive to make the reduced order model of any value compared to the full model. The second method, QLMOR, transforms the full model to a canonical QLDAE representation and performs Volterra analysis to derive a reduced model. We develop an algorithm that can mechanically transform a set of nonlinear differential equations to another set of equivalent nonlinear differential equations that involve only quadratic terms of state variables, and therefore it avoids any problem brought by previous Taylor-expansion-based methods. With the QLDAE representation, we develop the corresponding model order reduction algorithm that extends and generalizes previously-developed Volterra-based technique. The third method, NTIM, derives a macromodel that specifically captures timing/phase responses of a nonlinear system. We rigorously define the phase response for a non-autonomous system, and derive the dynamics of the phase response. The macromodel emerges as a scalar, nonlinear time-varying differential equation that can be computed by performing Floquet analysis of the full model. With the theory developed, we also present efficient numerical methods to compute the macromodel. The fourth method, DAE2FSM, considers a slightly different problem -- finite state machine abstraction of continuous dynamical systems. We present an algorithm that learns a Mealy machine from a set of differential equations from its input-output trajectories. The algorithm explores the state space in a smart way so that it can identify the underlying finite state machine using very few information about input-output trajectories.

The non-determinism in concurrent and distributed systems and the unreliability of the hardware environment in which they operate can result in defects that are hard to find and understand. In this thesis, we have developed tools and techniques to augment testing to enable it to quickly find and reproduce important bugs in concurrent and distributed systems.

This thesis mainly explores two fully integrated terahertz transceivers for sensing and communication applications in well matured 0.13 μm BiCMOS and 65 nm digital CMOS technology. Since antenna size shrinks quadratically as radiation frequency increases for a given gain, on-chip antennas have great potential in terahertz range by eliminating packaging issues for cost-effective, compact terahertz transceivers.

The conventional planar bulk MOSFET is difficult to scale down to sub-20nm gate length, due to the worsening performance variability and short channel effects. Thin body transistors, including Multiple-Gate (FinFET & Tri-Gate FET) and Fully Depleted SOI (FD-SOI) MOSFETs are anticipated to replace the current transistor architecture, and will be used in future CMOS technology nodes. Strained Silicon technology is widely used today to boost planar bulk transistor performance. Thus it's technically important to examine the strain-induced performance enhancement in these thin body transistors, for nanometer scale channel length. A comprehensive study on impact of channel stress on ultra-thin-body FD-SOI MOSFETs is presented. It's found that strain-induced mobility enhancement diminishes with Silicon body thickness scaling below 5nm for electrons, but not for holes. Strain-induced carrier transport enhancement is maintained with gate-length scaling. By applying forward back biasing (FBB) through the ultra-thin Buried Oxide layer, both carrier mobilities and their responses to strain get enhanced. For Multiple-gate FETs, the impact of performance enhancement through various types of stressors (including CESL, SiGe Source/Drain, Strained SOI and Metal Gate Last process) is studied, for different fin crystalline orientations and aspect ratios, to provide guidance for 3-D transistor design optimization.

This thesis presents three new computational tools that bring the strength of hybrid dynamical models and optimal control to applications in Cyber-Physical systems. The first tool is an algorithm that nds the optimal control of a switched hybrid dynamical system under state constraints, the second tool is an algorithm that approximates the trajectories of autonomous hybrid dynamical systems, and the third tool is an algorithm that computes the optimal control of a nonlinear dynamical system using pseudospectral approximations. These results achieve several goals. They extend widely used algorithms to new classes of dynamical systems. They also present novel mathematical techniques that can be applied to develop new, computationally efficient, tools in the context of hybrid dynamical systems. More importantly, they enable the use of control theory in new exciting applications, that because of their number of variables or complexity of their models, cannot be addressed using existing tools.

In  this  work,  Tunnel  Field  Effect  Transistor  (TFET)  based  on  Band-to-Band  Tunneling (BTBT) will be proposed and investigated as an alternative logic switch which can achieve steeper switching characteristics than the MOSFET to permit for lower threshold (VTH) and supply voltage (VDD) operation.

This thesis sprung up from the interest to immerse myself in the inner-workings of the wireless sensor networks (WSNs) that I was deploying as part of my Ph.D. dissertation in Civil Engineering.

In this thesis, the tunneling field-effect-transistor (TFET) is explored to replace conventional MOSFETs for low power applications. The band-to-band tunneling mechanism is looked into in order to develop a more accurate tunneling model that considers the change in effective mass during the transition between the conduction and valence band.