电信业务总量保持稳步增长，主营业务收入增速持续回落。2014年4月份，电信业务总量完成1459亿元，同比增长15.8%，比上年同期提高3.2个百分点；电信主营业务收入完成1018.4亿元，同比增长5.9%,比3月、上年同期分别回落0.3、2.3个百分点。

工信部网站5月16日报道，为更好地方便用户查询移动通信转售企业的电信资费信息，保障明明白白消费，同时，也为了能进一步规范虚拟运营商的电信资费等方面的工作，工信部于5月16日出台了《关于加强移动通信转售企业电信资费网上公示管理工作的通知》。

本刊以七大战略性新兴产业——节能环保、新一代信息技术、生物、高端装备制造、新能源、新材料、新能源汽车为研究重点，关注国家高层和各部委的动态，剖析国家和地方的新兴产业政策。本刊设有政策导读、领导讲话、七大战略性新兴产业本周国内外行业动态和重点企业新闻、投资专题四大板块，能够实时监测新兴产业和重点企业动态，把握新兴产业发展方向，研究发展重点，寻求发展机遇。

This research program has been focused on advanced technologies for detection and discrimination of military munitions. The underlying premise of the program has been that there is an inherent limitation in the information content associated with magnetometer and EMI sensors deployed for UXO cleanup. To optimize UXO classification one must integrate all available information, both within the measured data itself and within a priori knowledge one may possess. An important class of prior knowledge is represented by the sensor physics, and by placing as much physics as possible into the models and classification features, one removes the need to rely on the limited sensor data to infer such phenomenology. Statistical classifiers are also required to maximize the information extracted from the measured data to infer the unknown model parameters. Further, the statistical classifiers may be used to appropriately exploit other forms of information inherent to the data. For example, while performing classification one may exploit the contextual information provided by all of the unlabeled data at a given site, while also appropriately leveraging related information in data measured at previous sites.

In many remote sensing applications, the area of a scene sensed by a single pixel can often be measured in square meters. This means that many objects of interest in a scene are smaller than a single pixel in the resulting image. Current tracking methods rely on robust object detection using multi- pixel features. A subpixel object does not provide enough information for these methods to work. This dissertation presents a method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically sampled spatially. Using template matching, we estimate the maximum a posteriori probability of the target state over a sequence of images. A distance transform is used to calculate the motion prior in linear time, dramatically decreasing computation requirements. We compare the results of this method to a previously state-of-the-art track-before-detect particle filter designed for tracking small, low contrast objects using both synthetic and real-world imagery. Results show our method produces more accurate state estimates and higher detection rates than the current state of the art methods at signal-to-noise ratios as low as 3dB.

Military conflicts are shifting from jungles and deserts to cities. This is because terrorists, insurgents, and guerrillas find that these areas provide a rich target environment and good hideouts. With the use of UAVs, urban threats can be effectively tracked and targeted. However, in urban environments where there is little or no GPS signal and many obstacles, navigation of UAVs is a major challenge. Multiple UAVs can be employed to share sensor information to counter these challenges and to perform Intelligence, Surveillance, and Reconnaissance (ISR) missions with greater ground coverage and better success rates. This thesis explores the various types of UAVs deployed for urban operations, and investigates trends in the design of such UAVs in terms of their weight, altitude, speed, and sensor suite. The thesis discusses the challenges and requirements for interoperability of multi-UAVs in urban environments, and proposes a direct-method-based control system for multiple UAV collaboration and obstacle collision avoidance. A dynamic model was developed for the simulation testing of the control system algorithm, which was followed by a physical experiment in an indoor environment using Quanser QBall-X4 UAVs to evaluate the results. Results show that the UAVs were able to share and integrate their sensors' information for joint cooperation.

The subject research was performed at the University of Florida between December 2005 and December 2008. The research was performed to support the ability to detect landmines in an automated fashion using ground- penetrating radar (GPR) array sensors employed in systems being studied by NVESD. The work was concerned with discovering and evaluating i) different types of features that, when extracted from signals associated with GPR signals captured over regions of earth, can help one identify the presence or absence of landmines and landmine-like objects; (ii) algorithms and techniques that can employ these features to distinguish between landmines and non-mines; and (iii) fuse the results of multiple discriminators to yield improved discrimination performance.

The United States roadway system has deteriorated over time due to its age, increasing delays in completing preventative maintenance, and the lack of timely repairs following damage to the infrastructure. Proper asset management drives the need for generalized methods to integrate new sensing capabilities into existing Intelligent Transportation Systems in a time efficient and cost effective manner. In this thesis, we present a methodology for the deployment of new sensors into an existing ITS system. The proposed methodology employs a three phase approach that incorporates data modeling, spatial analysis in Geographic Information Systems, and cost optimization to provide enhanced decision support when deploying new sensing capabilities within an existing ITS. Additionally, we also demonstrate the usefulness of computing while integrating these new sensors using a guardrail sensor case study and focusing on data modeling. The results of the three phase methodology demonstrate an effective means for planning new sensor deployments by analyzing tradeoffs in equipment selection yielding the minimum cost solution for a given set of requirements. Furthermore, the results of the data models demonstrate necessary considerations that must be made with a systems engineering method. The data models accomplish this while accounting for asset management principles taking a systematic approach and incorporating engineering principles.

Laser Radar sensors can be designed to provide two-dimensional and three-dimensional (3-D) images of a scene from a single laser pulse. Currently, there are various data recording and presentation techniques being developed for 3-D sensors. While the technology is still being proven, many applications are being explored and suggested. As technological advancements are coupled with enhanced signal processing algorithms, it is possible that this technology will present exciting new military capabilities for sensor users. The goal of this work is to develop an algorithm to enhance the utility of 3-D Laser Radar sensors through accurate ranging to multiple surfaces per image pixel while minimizing the effects of diffraction. Via a new 3-D blind deconvolution algorithm, it will be possible to realize numerous enhancements over both traditional Gaussian mixture modeling and single surface range estimation. While traditional Gaussian mixture modeling can effectively model the received pulse, we know that its shape is likely altered due to optical aberrations from the imaging system and the medium through which it is imaging. Simulation examples show that the multi-surface ranging algorithm derived in this work improves range estimation over standard Gaussian mixture modeling and frame-by-frame deconvolution by up to 89and 85respectively.

Due to the United States' dependency on maritime travel, the proliferation of efficient and inexpensive naval mines poses a tremendous risk. Current mine countermeasure (MCM) technologies have a narrow field of view, preventing timely, wide-area searches. These technologies require the operator to be in proximity to the targets, a dangerous scenario made worse when in denied territory. In an effort to mitigate these risks, the use of an airborne hyperspectral sensor is proposed. The operational ability of a hyperspectral sensor to detect sub-pixel surface and submerged mines in non-littoral environments was evaluated using two common anomaly detectors: Mixture Tuned Matched Filtering (MTMF) and Reed-Xiaoli (RX). Due to the unavailability of the DoD's Spectral Infrared Imaging Technology Testbed (SPIRITT), ProSpecTIR-VS3, a sensor similar spatially and spectrally to SPIRITT was flown over a Navy test range offshore California. This experiment included three surface and three submerged targets, each with a 0.8 meter diameter. The spatial resolution of the images is dependent on the altitude of the sensor. In an effort to collect both a high spatial resolution and a low spatial resolution data set, two flight altitudes were planned. The high spatial resolution collection altitude was approximately 410 meters and the low spatial resolution altitude was approximately 800 meters. The spatial resolutions of the collections were 0.5 and 1.0 meters, respectively. This allowed for both a resolved and an unresolved analysis. While both anomaly detection techniques were found to have their flaws, the success of the study is in proving the usefulness of hyperspectral data for sub-pixel mine detection.

Differential Global Positioning Systems (DGPS) are susceptible to outages due to blocked or missing satellite signals and/or blocked or missing DGPS correction messages. Outages arise primarily due to environmental reasons: passing under bridges, passing under overhead highway signs, adjacent foliage, etc. Generally, these outages are spatially deterministic, and can be accurately predicted. These outages distract drivers using DGPS-based driver assistive systems, and limit the system robustness. Inertial measurements have been proposed as an augmentation for DGPS. Tests have shown that error rates for even emerging technologies are still too high; a vehicle can maintain lane position for less than three to four seconds. Ring laser gyros can do the job, but $100K per axis is still too expensive for road-going vehicles. To provide robust vehicle positioning in the face of DGPS outages, the IV Lab has developed a technique by which a non-contact, 2D true ground velocity sensor is used to guide the vehicle. Although far from fully developed, the system can maintain vehicle position within a lane for GPS outages of up to 20 seconds. New dual frequency, carrier phase DGPS systems generally require less than 20 seconds to acquire a 'fix' solution after a GPS outage, so the performance of this system should be adequate for augmentation. Proposed herein is basic research which may lead to the development of an inexpensive, 2D, non-contact velocity sensor optimized for vehicle guidance during periods of DGPS outages.

The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi- sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster- Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur.