In this paper, an opportunistic cooperative ad hoc sensor network with randomly located nodes is analyzed. The randomness of nodes' locations is captured by a homogeneous Poisson point process. The effect of imperfect interference cancellation is also taken into account in the analysis. Based on the theory of stochastic geometry, outage probability and cooperative gain are derived. It is demonstrated that explicit performance gain can be achieved through cooperation. The analyses are corroborated by extensive simulation results and the analytical results can thus serve as a guideline for wireless sensor network design.

Large-scale MIMO (multiple-input multiple-output) systems with numerous low-power antennas can provide better performance in terms of spectrum efficiency, power saving and link reliability than conventional MIMO. For large-scale MIMO, there are several technical issues that need to be practically addressed (e.g., pilot pattern design and low-power transmission design) and theoretically addressed (e.g., capacity bound, channel estimation, and power allocation strategies). In this paper, we analyze the sum rate upper bound of large-scale MIMO, investigate its key technologies including channel estimation, downlink precoding, and uplink detection. We also present some perspectives concerning new channel modeling approaches, advanced user scheduling algorithms, etc.

It is generally acknowledged that mobile communication base stations are composed of hardware components such as Field Programming Gate Array (FPGA), Digital Signal Processor (DSP), which promise reliable and fluent services for the mobile users. However, with the increasing demand for energy-efficiency, approaches of low power-consumption and high-flexibility are needed urgently. In this circumstance, General Purpose Processor (GPP) attracts people's attention for its low-cost and flexibility. Benefited from the development of modern GPP in multi-core, Single Instruction Multiple Data (SIMD) instructions, larger cache, etc., GPPs are capable of performing high-density digital processing. In this paper, we compare several software-defined radio (SDR) prototypes and propose the general architecture of GPP-based soft base stations. Then, the schematic design of resource allocation and algorithm optimization in soft base station implementation are studied. As an application example, a prototype of GPP-based soft base station referring to the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is realized and evaluated. To the best of our knowledge, it is the first Soft-LTE prototype ever reported. In the end, we evaluate the timing performance of the LTE soft base station and a packet loss ratio of less than 0.003 is obtained.

General purpose processer (GPP) based software-defined radio (SDR) platforms provide wireless communication system engineers with maximal architecture flexibility and versatility to construct a wideband wireless communication system. Nevertheless, the lack of hardware real-time timing control makes it difficult to achieve time synchronization between the base station and the terminals. In this paper, a software-based time synchronization (STS) method is proposed to realize the time synchronization of time division multiple access (TDMA) based wireless communication systems. A high precision software clock source is firstly constructed to measure the elapse of processing time. The Round-Trip Delay (RTD) algorithm is then presented to calculate timing advance values and achieve time synchronization. An example TDMA system is implemented on Microsoft Sora platforms to evaluate the performance. Experiments show that the proposed mechanism is effective to enable time synchronization for wideband wireless communication systems on GPP-based SDR platforms.

With the development of mobile internet and multi-media service, advanced techniques need to be applied in wireless network to improve user experience. Long term evolution (LTE) systems, which can offer up to 100Mbps downlink date rates, have been deployed in USA and Korea. However, because plenty of complex physical layer algorithms are utilized, network planning and optimization become heavy burdens for LTE network operators. Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems. In this paper, we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF). Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme. Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly.

In this article, two methods adopting simplified minimum mean square error (MMSE) filter with soft parallel interference cancellation (SPIC) are discussed for turbo receivers in bit interleaved coded modulation (BICM) multiple-input multiple-output (MIMO) systems. The proposed methods are utilized in the non-first iterative process of turbo receiver to suppress residual interference and noise. By modeling the components of residual interference after SPIC plus the noise as uncorrelated Gaussian random variables, the matrix inverse for weighting vector of conventional MMSE becomes unnecessary. Thus the complexity can be greatly reduced with only slight performance deterioration. By introducing optimal ordering to SPIC, performance gap between simplified MMSE and conventional MMSE further narrows. Monte Carlo simulation results confirm that the proposed algorithms can achieve almost the same performance as the conventional MMSE SPIC in various MIMO configurations, but with much lower computational complexity.

An explicit lighting estimation from a single image of Lambertian objects is influenced by two factors: data incompletion and noise contamination. Measurement of lighting consistency purely using the orthogonal spherical harmonic basis cannot achieve an accurate estimation. We present a novel signal-processing framework to represent the lighting field. We construct a redundant spherical harmonic frame with geometric symmetry on the sphere S². Spherical harmonic frames are defined over the generating rotation matrices about symmetry axes of finite symmetry subgroups of SO(3), and the generating functions are spherical harmonic basis functions. Compared with the orthogonal spherical harmonic basis, the redundant spherical harmonic frames not only describe the multidirectional lighting distribution intuitively, but also resist the noise theoretically. Subsequently, we analyze the relationship of the irradiance to the incoming radiance in terms of spherical harmonic frames, and reconstruct the lighting function filtered by the Lambertian BRDF (bidirectional reflectance distribution function). The experiments show that the frame coefficients of spherical harmonic frames can better characterize the complex lighting environments finely and robustly.

Labeling connected components and holes and computing the Euler number in a binary image are necessary for image analysis, pattern recognition, and computer (robot) vision, and are usually made independently of each other in conventional methods. This paper proposes a two-scan algorithm for labeling connected components and holes simultaneously in a binary image by use of the same data structure. With our algorithm, besides labeling, we can also easily calculate the number and the area of connected components and holes, as well as the Euler number. Our method is very simple in principle, and experimental results demonstrate that our method is much more efficient than conventional methods for various kinds of images in cases where both labeling and Euler number computing are necessary.

3D caricatures are important attractive elements of the interface in virtual environment such as online game. However, very limited 3D caricatures exist in the real world. Meanwhile, creating 3D caricatures manually is rather costly, and even professional skills are needed. This paper proposes a novel and effective manifold transfer algorithm to reconstruct 3D caricatures according to their original 2D caricatures. We first manually create a small dataset with only 100 3D caricature models and use them to initialize the whole 3D dataset. After that, manifold transfer algorithm is carried out to refine the dataset. The algorithm comprises of two steps. The first is to perform manifold alignment between 2D and 3D caricatures to get a "standard" manifold map; the second is to reconstruct all the 3D caricatures based on the manifold map. The proposed approach utilizes and transfers knowledge of 2D caricatures to the target 3D caricatures well. Comparative experiments show that the approach reconstructs 3D caricatures more effectively and the results conform more to the styles of the original 2D caricatures than the Principal Components Analysis (PCA) based method.

This paper presents an efficient method to trace secondary rays in depth-of-field (DOF) rendering, which significantly enhances realism. Till now, the effects by secondary rays have been little addressed in real-time/interactive DOF rendering, because secondary rays have less coherence than primary rays, making them very difficult to handle. We propose novel measures to cluster secondary rays, and take a virtual viewpoint to construct a layered image-based representation for the objects that would be intersected by a cluster of secondary rays respectively. Therefore, we can exploit coherence of secondary rays in the clusters to speed up tracing secondary rays in DOF rendering. Results show that we can interactively achieve DOF rendering effects with reflections or refractions on a commodity graphics card.

Laparoscopic surgery has many advantages, but it is difficult for a surgeon to achieve the necessary surgical skills. Recently, virtual training simulations have been gaining interest because they can provide a safe and efficient learning environment for medical students and novice surgeons. In this paper, we present a hybrid modeling method for simulating gallbladder removal that uses both the boundary element method (BEM) and the finite element method (FEM). Each modeling method is applied according to the deformable properties of human organs: BEM for the liver and FEM for the gallbladder. Connective tissues between the liver and the gallbladder are also included in the surgical simulation. Deformations in the liver and the gallbladder models are transferred via connective tissue springs using a mass-spring method. Special effects and techniques are developed to achieve realistic simulations, and the software is integrated into a custom-designed haptic interface device. Various computer graphical techniques are also applied in the virtual gallbladder removal laparoscopic surgery training. The detailed techniques and the results of the simulations are described in this paper.

Improving the network interface performance is needed by the demand of applications with high communication requirements (for example, some multimedia, real-time, and high-performance computing applications), and the availability of network links providing multiple gigabits per second bandwidths that could require many processor cycles for commu- nication tasks. Multicore architectures, the current trend in the microprocessor development to cope with the difficulties to further increase clock frequencies and microarchitecture efficiencies, provide new opportunities to exploit the parallelism available in the nodes for designing efficient communication architectures. Nevertheless, although present OS network stacks include multiple threads that make it possible to execute network tasks concurrently in the kernel, the implementations of packet-based or connection-based parallelism are not trivial as they have to take into account issues related with the cost of synchronization in the access to shared resources and the efficient use of caches. Therefore, a common trend in many recent researches on this topic is to assign network interrupts and the corresponding protocol and network application processing to the same core, as with this affinity scheduling it would be possible to reduce the contention for shared resources and the cache misses. In this paper we propose and analyze several configurations to distribute the network interface among the different cores available in the server. These alternatives have been devised according to the affinity of the corresponding communi- cation tasks with the location (proximity to the memories where the different data structures are stored) and characteristics of the processing core. As this approach uses several cores to accelerate the communication path of a given connection, it can be seen as complementary to those that consider several cores to simultaneously process packets belonging to either the same or different connections. Message passing interface (MPI) workloads and dynamic web servers have been considered as applications to evaluate and compare the communication performance of these alternatives. In our experiments, performed by full-system simulation, improvements of up to 35% in the throughput and up to 23% in the latency have been observed in MPI workloads, and up to 100% in the throughput, up to 500% in the response time, and up to 82% in the requests attended per second have been measured in dynamic web servers.

High-speed wireless networks such as IEEE 802.11n have been introduced based on IEEE 802.11 to meet the growing demand for high-throughput and multimedia applications. It is known that the medium access control (MAC) efficiency of IEEE 802.11 decreases with increasing the physical rate. To improve efficiency, few solutions have been proposed such as Aggregation to concatenate a number of packets into a larger frame and send it at once to reduce the protocol overhead. Since transmitting larger frames eventuates to dramatic delay and jitter increase in other nodes, bounding the maximum aggregated frame size is important to satisfy delay requirements of especially multimedia applications. In this paper, we propose a scheme called Optimized Packet Aggregation (OPA) which models the network by constrained convex optimization to obtain the optimal aggregation size of each node regarding to delay constraints of other nodes. OPA attains proportionally fair sharing of the channel while satisfying delay constrains. Furthermore, reaching the optimal point is guaranteed in OPA with low complexity. Simulation results show that OPA can successfully bound delay and meet the requirements of nodes with only an insignificant throughput penalty due to limiting the aggregation size even in dynamic conditions.

Many production peer-to-peer (P2P) streaming systems use content delivery networks (CDN) to protect the user's quality of experiences. Thus, how to efficiently utilize the capacity of CDN (e.g., which peers receive services from the CDN nodes) is a problem of practical significance. Existing solutions adopt a passive, on-demand approach, which is inefficient in utilizing CDN resources. In this paper, we propose PROSE, a simple, novel scheme to achieve proactive, selective CDN participation for P2P streaming. PROSE introduces novel concepts such as choke point expansion nodes/super nodes and leads to efficient, light-weighted, and distributed algorithms to identify and serve these nodes using CDN. Our experimental results show that PROSE achieves at least 10%～25% performance improvement and 2～4 times overhead reduction compared with existing general CDN-P2P-hybrid schemes.

In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the quality of monitoring (QoM) of the network. In this paper, a particle swarm optimization (PSO)-based solution is proposed to achieve the optimal channel selection. A 2D mapping particle coding and its moving scheme are devised. Monte Carlo method is incorporated to revise the solution and significantly improve the convergence of the algorithm. The extensive simulations demonstrate that the Monte Carlo enhanced PSO (MC-PSO) algorithm outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.

In wireless sensor networks, a clustering scheme is helpful in reducing the energy consumption by aggregating data at intermediate sensors. This paper discusses the important issue of energy optimization in hierarchically-clustered wireless sensor networks to minimize the total energy consumption required to collect data. We propose a comprehensive energy consumption model for multi-tier clustered sensor networks, in which all the energy consumptions not only in the phase of data transmissions but also in the phase of cluster head rotations are taken into account. By using this new model, we are able to obtain the solutions of optimal tier number and the resulted optimal clustering scheme on how to group all the sensors into tiers by the suggested numerical method. This then enables us to propose an energy-efficiency optimized distributed multi-tier clustering algorithm for wireless sensor networks. This algorithm is theoretically analyzed in terms of time complexity. Simulation results are provided to show that, the theoretically calculated energy consumption by the new model matches very well with the simulation results, and the energy consumption is indeed minimized at the optimal number of tiers in the multi-tier clustered wireless sensor networks.

Routing is one of the challenging tasks in Delay Tolerant Networks (DTNs), due to the lack of global knowledge and sporadic contacts between nodes. Most existing studies take a greedy scheme in data forwarding process, i.e., only nodes with higher utility values than current carriers can be selected as relays. They lack an in-depth investigation on the main features of the optimal paths in Epidemic. These features are vital to any forwarding scheme that tends to make a trade-off between packet delivery delay and cost. This is mainly because Epidemic provides an upper bound on cost and a lower bound on delivery delay. Therefore, a deep understanding of these features is useful to make informed forwarding decisions. In this paper, we try to explore these features by observing the roles of different social relationships in the optimal paths through a set of real datasets. These datasets provide evidence that strangers have two sides in data forwarding process, and that the importance of strangers shows a decreasing trend along the forwarding paths. Using this heuristic knowledge, we propose STRON, a distributed and lightweight forwarding scheme. The distributed feature makes it very suitable for opportunistic scenarios and the low communication and computation features make it easy to be integrated with state-of-the-art work. The trace-driven simulations obviously confirm its effectiveness, especially in terms of packet delivery delay and cost.