Nowadays, we get more and more remote sensing (RS) images which cannot be well processed or used by manual analysis or existing automatic methods. In the past few years, the object detection technology has greatly developed, especially after the usage of CNN in object detectors. However, Object detection in large scale RS images is still a challenging tasks which needs further study. Compared to natural images, RS images include much more objects in different sizes with a larger scope. Therefore, it is extraordinarily time-consuming to detect small objects in a large-scale RS image, since this work needs more scale and location traverses. Algorithms for common images cannot tackle the problem of some special object detection, like aircraft detection, in RS images. In this paper, we introduce an extra Region Proposal strategy named Region Locating Network (RLN) to improve the Faster RCNN framework. The proposed RLN locates spectacular areas where aircrafts are usually found, like parts of the runway and the parking apron. Based on the locating result, we can use Faster RCNN to detect airplanes in several smaller image regions. Extensive experiments show that the proposed method has oblivious improvement in recall rate, accuracy and computing efficiency.

A user-centric no-cell (UCNC) framework has been proposed which enables different level coordination over a large number of TRPs. This paper presents some key technologies in UCNC framework with the focus on PoC of UL grant- free transmissions in UCNC architecture. Massive connectivity support is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant- free transmission based on SCMA (sparse code multiple access) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectiv- ity requirements. Extensive lab tests have been conducted to verify the performance matrices. Taken LTE as a baseline for comparison purpose, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

A 5G mobile network has evolved from a cell centric radio access network to a user centric one. A user-centric no-cell (UCNC) framework has been proposed, which enables physical layer coordination over a large number of transmission and reception points for each mobile user to always experience cell-center-like communications. This paper presents some key technologies in the UCNC framework. The focus is on proof of concept of UL grant-free transmissions in the UCNC architecture. Massive connectivity supporting a huge number of devices is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant-free transmission based on sparse code multiple access (SCMA) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectivity requirements. Grant-free transmission conducted in RRC connected state can reduce signaling overhead, while in energy conserved operation (ECO) state, it can significantly reduce the user plane transmission latency. Extensive laboratory testing has been conducted in a collaborative project of Huawei and Telefonica to verify the performance matrices. Taking LTE as a baseline for comparison purposes, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with the SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

Software Defined Network (SDN) is deemed to be one of the promising solutions for the next-generation wireless communication system. Software Defined Protocol (SDP) is the data plane equivalent of SDN. The design objective of SDP is to make data processing protocol stacks programmable based on platform virtualization and functionality modularization to redefine the wireless network architecture. In this paper, a protocol module multiplexing algorithm for SDP is proposed, where all the function blocks of protocol processing are abstracted to protocol modules and resources are optimally allocated to the protocol modules, targeting to minimize the total processing time of data flows. Numerical simulations show that the proposed module multiplexing algorithm significantly outperforms the baseline algorithm in terms of throughput. The baseline is set as the traditional algorithm in which resources are allocated to all modules uniformly. Throughput gains about 156% and 128% over the baseline are observed, on uplink and downlink respectively. It is also observed that the throughput of the module multiplexing algorithm increases as the total module number increases.

Aircraft detection in satellite images is generally difficult due to the variations of aircraft type, pose, size and complex background. In this paper, we propose a new aircraft detection framework based on objectiveness detection techniques (e.g., BING) and Convolutional Neural Networks (CNN). The advantages are two folds. On one hand, we first introduce the CNN for aircraft detection, as CNN can learn rich features from the raw data automatically and has yielded a state-of-the-art performance in many object detection tasks. On the other hand, the use of candidate object regions proposed by BING achieves a high object detection rate and saves time simultaneously. Experimental results show that the proposed method is fast and effective to detect aircrafts in complex airport scenes. We also construct a dataset for aircraft detection obtained from Google Earth.

The land use and land cover classification is an important and hot research topic in remote sensing image processing. How to use information effectively in remote sensing data to categorize different land use and land cover scenes needs urgent attention. In this paper, we analysis the Bag-of-Word model based feature extracting method systematically, and propose the Saliency Map Cooperated (SMC) coding strategy according characters of remote sensing images. The proposed SMC takes into account both the primary objects and partial of large scale objects in remote sensing images, with little effecting on texture dominated images. Extensive experimental results show the efficiency of the proposed SMC coding strategy.

Road information can be used for several applications, such as essential modes of transportation and updating geographic information systems (GIS) from VHR remote sensing images. In recent years, with the development of multi-sensor and multi-resolution remote sensing techniques, we have acquired a large quantity of high-resolution satellite images. Accurate road extraction from VHR sensing image has drawn considerable attention and a great deal of effort has been devoted to this topic.

With the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be obtained and widely used in military and civilian fields. Detecting typical targets in satellite images is a challenging task due to the complicated background. Traditional manually engineered features (i.e. HOG, Gabor feature and Hough transform, etc.) do not work well for massive high-resolution remote sensing image data. Thus, we are expected to find an efficient way to automatically learn the presentations from the massive image data and increase the computational efficiency of target detection. Comparing to the general objects in nature images, the edge information of targets in satellite images shows more distinctive and concise characteristics. This paper proposes a new target detection framework based on Edge Boxes and Convolutional Neural Networks (CNN). CNN can learn rich features automatically and is invariant to small rotation and shifts, has achieved state of-the-art performance in many image classification databases. Edge Boxes can generate a smaller set of object proposals based on the edges of objects. The proposed method can reduce the computational time of the detector. Extensive experiments demonstrate that the proposed framework is effective in typical target detection systems.

Bridges are the hubs of transportation, so it is important to identify and locate bridges for satellite image interpretation. This paper proposes a new method of bridge extraction from high resolution remote sensing images. Firstly, river regions are segmented and road lineaments are extracted. Then, bridge region is represented as intersection of river with roads or roads with roads by using the recognition model proposed in this paper. Finally, a rule-based procedure is applied to verify candidate regions. The experiment results show that, not only bridges over river but also the overpasses can be extracted effectively. The method mainly uses structural information of lineaments on roads and the topological relations in bridge regions, and does not rely on accurate results of river segmentation, so it is robust for complex scenes.

The contours of polygons generated by the image segmentation technique show jagged outlines and a large number of redundant points. Therefore, the original segmentation contours hardly conform to geographic information system (GIS) data-producing standards without generalization. With the complexity of high spatial resolution remote sensing imagery data, with variable sizes of geographic features and their different distributive patterns, it is hard to build a global contour optimization parameter model to guide parameter settings in large regions effectively. Furthermore, it is also difficult to automatically give a unique set of parameters per object simultaneously. In order to meet the actual requirements of GIS data production, we present an adaptively improved algorithm based on the Douglas-Peucker (DP) algorithm, named AIDP, that integrates the criteria of vertical and radial distance restriction, and design a corresponding parameter-adaptive acquisition method. The proposed AIDP method is evaluated by comparing it with the most widely used DP algorithm implemented in the ArcGIS through visual inspection, quantitative measurements, and applications to water body contours. The experimental results show that AIDP can not only acquire generalization parameters automatically but also greatly speed up the data processing workflow with acceptable results.

Rate control and adaptation is an important issue for video communications over cellular networks. In this paper, we propose a quality-aware feedback-free rate control and adaptation scheme for downlink real-time video communications over Long Term Evolution (LTE) networks. In our scheme, the send bit rate is adapted automatically according to the estimated packet loss due to the expiration of packet delay deadline based on queueing analysis by taking into account both packet queueing delay and transmission delay. This differs from existing rate adaptation methods which adapt send bit rate according to either packet loss or delay information explicitly feedback from the user side. The proposed scheme can maximize the system capacity in terms of the number of supportable users while providing satisfying user experience in terms of the received video quality and playback smoothness. Experimental results demonstrate the effectiveness of the proposed scheme.

In order to obtain complete and correct information for road line extraction from remote sensing image, the complementary of two kinds of line feature is discussed and a feature fusion method is proposed. The method is implemented by cross validating line features of the LSD and the SRM according their spatial relationship. The results of experiments demonstrated the complementary of these two kinds features, and the performance of road extraction with the fused features can be improved obviously than with any single kind of line feature.

A theory of multiparty sessions is presented in this paper, where the semantic analysis of global types ensures that the projection onto each participant preserves a strong fairness assumption, i.e., no participant of multiparty sessions will wait for messages that are never sent or will send messages that no other participant will receive. In addition, once a global type is agreed upon by all participants involved, a local protocol from each local viewpoint will be generated as a projection onto each participant using a top-down approach. Furthermore, the introduction of asynchronous communication sub typing allows permutation of actions to raise efficiency, where each local type can be locally refined using our action asynchronous sub typing rules for both dependent and independent messages, which gives a more optimized protocol.

In this paper a crossed application and physical layer retransmission scheme is proposed for video streaming services in hybrid wireline and wireless networks. Each video packet carries the importance levels of succeeding packets at the application layer. In wireless transmission, by mapping the packet importance levels to different number of physical layer HARQ retransmissions, data packets are differentially protected which can greatly alleviate higher layer retransmissions and at the same time reduce the transmission delay for realtime services.

Recent technological advances in code division multiple access (CDMA) with multiuser detection (MUD) allow to consider this technology for future wireless Ad Hoc networks. Due to the fundamentally different physical layer architecture, application of MUD in Ad Hoc networks requires novel approaches for medium access control (MAC) and scheduling mechanisms in order to take advantage of the new features. This paper proposes a new MAC and scheduling paradigm which addresses three design issues: distributed dynamic code assignment that avoids code collision, distributed scheduling scheme that provides fairness among contending nodes, and organization of the data transmission based on multiuser detection. Simulation model is used to verify the performance gains from the increased spectrum reuse due to multiuser reception and from the reduced interference due to multiuser detection. This is done by comparisons with existing MAC paradigms, such as CSMA/CA and parallel CDMA.

Recent technological developments in code division multiple access (CDMA) with multiuser detection (MUD) make multiple packets reception a more appropriate model for the physical layer of future wireless networks. To take advantage of the new features, a shift of responsibility from transmitters to

Least visible path analysis is a basic function in terrain visibility analysis. However, current least visible path planning is constrained to least‐cost path computing on a cost surface obtained from visibility information of all the terrain points on digital elevation models. This kind of method ignores the visibility correlation caused by the overlapped part of the adjacent points' reverse viewsheds. With regard to such a correlation, this paper proposes a new method using the amalgamation of reverse viewsheds to find an optimal path with the minimal visibility dominance. Both methods are implemented using C++ programming, and the experimental results show that the new method produces more accurate least visible paths than the traditional one does.

Path visual coverage is the region which could be seen from the path in the terrain. This paper advances the concept of "average horizon " which represents the wide extent of the visual field of each path point to analyze and model the max and min path visual coverage problems, and resolves them utilizing simulated annealing algorithm based on the operation of viewshed amalgamation with pre-computed and stored viewshed information.

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

An adaptive handoff priority scheme is proposed for a packet switching multicode (MC)-CDMA cellular network supporting multimedia applications. The scheme jointly considers the physical, link and network layer characteristics, and gives service priority to handoff calls by exploiting the transmission rate adaptability of multimedia services over the available radio resources. Based on the proposed scheme, an admission region is derived for call admission control (CAC) and handoff management with quality of service (QoS) satisfaction for all multimedia traffic, where the QoS parameters include the wireless transmission bit error rate (BER), packet loss rate (PLR) and delay requirement. Simulation results substantiate the viability of the proposed scheme.

A novel resource reservation scheme for handoff priority in code division multiple access (CDMA) wireless cellular networks is proposed. The reservation scheme is derived by minimizing a grade of service (GoS) objective function which adjoins new call blocking probability and handoff call dropping probability through an arbitrary multiplier. The system capacity is optimized by minimizing the GoS function and jointly taking call level GoS and packet level quality of service (QoS) into consideration. The resultant reservation scheme is implemented adaptively using user mobility information and variation in traffic load. Numerical results show that, with the satisfaction of new call blocking probability and GoS performance, cell capacity and cell utilization are significantly improved relative to a resource reservation scheme in which only the call level characteristics are taken in to account.