An efficient method for dynamic magnetic resonance imaging

Many magnetic resonance imaging applications require the acquisition of a time series of images. In conventional Fourier transform based imaging methods, each of these images is acquired independently so that the temporal resolution possible is limited by the number of spatial encodings (or data points in the Fourier space) collected, or one has to sacrifice spatial resolution for temporal resolution. Here, a generalized series based imaging technique is proposed to address this problem. This technique makes use of the fact that, in most time-sequential imaging problems, the high-resolution image morphology does not change from one image to another, and it improves imaging efficiency (and temporal resolution) over the conventional Fourier imaging methods by eliminating the repeated encodings of this stationary information. Additional advantages of the proposed imaging technique include a reduced number of radio frequency (RF) pulses for data collection, and thus lower RF power deposition. This method should prove useful for a variety of dynamic imaging applications, including dynamic studies of contrast agents and functional brain imaging.     

An efficient traffic control system using dynamic thresholding techniques in wireless mesh networks

Wireless mesh networks (WMNs) depend on a resilient and high‐performance infrastructure to provide users pervasive Internet access. In WMNs, all Internet traffic will be forwarded to the Internet gateways. Hence, these gateways are generally bottleneck nodes. This work proposes a traffic control technique to reduce the bottleneck problem and increase the utilization of network resources. Our approach provides a traffic control strategy that exploits dynamic techniques to adjust the threshold according to the traffic load of each gateway. The base threshold is defined in order to effectively control the traffic. When the current load exceeds the threshold of a gateway, the traffic redirection strategy is implemented by switching border nodes. The service regions can be adjusted for each gateway based on the traffic load. Furthermore, the proposed dynamic thresholding approaches can distribute the workloads of gateways and maintain the thresholds of any two gateways within a level range, making an in‐band balance of load. Thus, our proposed scheme can handle the unnecessary traffic redirection and reduce the traffic control overhead for various distributions of traffic. Experimental results demonstrate that our scheme outperforms other schemes in terms of packet delivery ratio and efficiency, especially in bursty traffic environments. Copyright © 2010 John Wiley & Sons, Ltd.     

An analysis of the dynamic behavior of switching circuits using avalanche transistors

The behavior of the basic switching circuit using avalanche transistors is solved analytically by suitably approximating the static I-V characteristic of the device. From the normalized expression of the output current pulse, a set of curves relating amplitude and width of the pulse to the transistor and circuit parameters is obtained. The conditions for bistability and maximum resolution are investigated giving the minimum resistance needed for monostable or astable operation. The analytical solution is compared with that numerically computed and with experimental results.     

基于大系统方法的上行CDMA蜂窝系统性能分析

结合大尺度和小尺度衰落,采用大系统分析方法对CDMA蜂窝系统上行链路的性能进行分析。首先分析了小区内多址干扰和小区间干扰的情况;其次推导出了通过匹配滤波器后的信干噪比值,该值只与小区负载、用户功率、信道增益有关;最后分析了系统的误码率、吞吐量和频谱效率,并通过仿真验证了分析的准确性。仿真结果发现,随着小区负载的增大,系统的误码率增大,吞吐量下降,但频谱效率有所提高。     

Spline-based behavioural model for efficient analysis of large front-ends for W-CDMA systems

A new behavioural model suitable for the analysis of RF front-ends for W-CDMA systems is described. The model fully accounts for both linear and nonlinear dispersion effects, so that relatively by a band digitally modulated signals can be accurately dealt with. The linear front-end response is computed by means of the conversion admittance matrix. The relationship between the linear and nonlinear responses is characterised by a voltage and frequency-dependent describing function. The latter is evaluated by a cubic-spline-based two-dimensional interpolation scheme starting from a database generated by harmonic-balance analysis. In this way, front-end simulations that would require months of CPU time even by modern envelope-oriented techniques, can be performed in a matter of seconds without significant loss of accuracy.     

一种控制系统性能指标的快速提取法

提出了一种直观的控制系统时域性能指标的提取方法。该方法是基于Matlab/Simulink平台,通过调用已编写好的m文件提取单项性能指标,运行已搭建好的Simulink模块读取积分误差性能指标。该方法具有很强的直观性和通用性,降低了控制系统性能指标获取的复杂度,提高了性能指标获取的精度。     

An efficient high-dimensional indexing method for content-based retrieval in large image databases

High-dimensional indexing methods have been proved quite useful for response time improvement. Based on Euclidian distance, many of them have been proposed for applications where data vectors are high-dimensional. However, these methods do not generally support efficiently similarity search when dealing with heterogeneous data vectors. In this paper, we propose a high-dimensional indexing method (KRA⁺-Blocks) as an extension of the region approximation approach to the kernel space. KRA⁺-Blocks combines nonlinear dimensionality reduction technique (KPCA) with region approximation approach to map data vectors into a reduced feature space. The created feature space is then used, on one hand to approximate regions, and on the other hand to provide an effective kernel distances for both filtering process and similarity measurement. In this way, the proposed approach achieves high performances in response time and in precision when dealing with high-dimensional and heterogeneous vectors.     

面向铁路场景的大规模点云高效去噪方法

利用车载激光雷达获取铁路沿线环境信息对于保障行车安全具有重要意义。但是激光雷达采集到的点云数据受设备及环境因素影响,会产生大量的噪声干扰,这些噪声严重影响后续的感知和监测任务。为此,本文提出了一种面向铁路场景的大规模点云高效去噪方法。该方法提出了一种新颖的网格投影策略,对大规模铁路点云数据进行高效的降维降采样处理。然后,设计了基于GPU的改进聚类加速算法,快速识别离群的噪声数据。最后综合设计多策略融合方法,有效地去除噪声数据。所提方法充分利用铁路场景前向运动的特点,对点云数据进行基于网格化的时空压缩,同时利用GPU加速聚类算法的矩阵运算,实现了实时高效的铁路场景大规模点云去噪算法。实验结果表明,所提方法不仅能够提高去噪的性能,而且处理效率得到了极大提升。     

An efficient dynamic distributed optical link establish method in intelligent all-optical networks

In this article, an efficient, fast, and dynamic distributed optical link setup method is proposed. Two kinds of novel optical fast link release mechanisms (Ahead and Timeout Release) are presented for an optical-link establishment. They can dramatically reduce the blocking probability. For dynamic routing detection, a new kind of dynamic weighted Dijkstra algorithm (DW-DA) along with dynamic optical link load balancing is described. A variable mutation and crossover rates of a genetic algorithm (VMCR-GA) is used for fast wavelength assignment with two-novel-wavelength assignment rules in a wavelength relationship graph, which can reduce the necessary wavelengths and link establishment time. Through simulation giving the blocking probability and the time for link establishment on several well-known networks, the effectiveness of this method has been verified. The blocking probability of the network can be reduced significantly below that of normal routing and wavelength assignment (RWA). Furthermore, the calculating time for reaching the minimum blocking probability can be reduced dramatically.

An efficient method for analysis of arbitrary nonuniformtransmission lines

The analytical solution of an ideal linear varied nonuniform transmission line (LNTL) has been obtained and the exact linear two-port ABCD matrix of LNTL has been given correctly for the first time. By using cascaded LNTL sections to approximate an arbitrary characteristic impedance profile, a new technique has been presented in this paper for analyzing an arbitrary nonuniform transmission line (NTL). The technique is far better than the conventional technique in terms of the computational accuracy and intensity since it uses a piecewise-linear characteristic impedance profile in place of the stepped profile used by the conventional technique. Several numerical examples have been given to demonstrate the method     

An analysis method for high-speed circuit systems

A new method for analyzing high-speed circuit systems is presented. The method adds transmission line end currents to the circuit variables of the classical modified nodal approach. Then the matrix equation describing high-speed circuit system can be formulated directly and analyzed conveniently for its normative form. A time-domain analysis method for transmission lines is also introduced. The two methods are combined together to efficiently analyze high-speed circuit systems having general transmission lines. Numerical experiment is presented and the results are compared with that calculated by Hspice.     

An efficient contrast enhancement method using repulsive force of edges

Multidimensional Systems and Signal Processing - This paper presents a novel algorithm which employs edge information to automatically improve the quality of low contrast images. This algorithm...     

An efficient method of cast shadow removal using multiple features

Features of images are often used for cast shadow removal. A technique based on using only a single feature cannot universally distinguish an object pixel from a shadow pixel of a video frame. On the other hand, the use of multiple features increases the computational cost of a shadow removal technique considerably. In this paper, an efficient yet simple method for cast shadow removal from video sequences with static background using multiple features is developed. The basic idea of the proposed technique is that a simultaneous use of a small number of multiple features, if chosen judiciously, can reduce the similarity between object and shadow pixels without an excessive increase in the computational cost. Using the features of gray levels, color composition, and gradients of foreground and background pixels, a method is devised to create a complete object mask. First, based on each of the three features, three individual shadow masks are constructed, from which three corresponding object masks are obtained through a simple subtraction operation. The object masks are then merged together to generate a single object mask. Each of the three shadow masks is created so as to cover as many shadow pixels as possible, even if it results in falsely including in them some of the object pixels. As a result, the subsequent object masks may lose some of these pixels. However, the object pixels missed by one of the object masks should be able to be recovered by at least one of the other two, since they are generated based on features complementary to the one used to construct the first one. The final object mask obtained through a logical OR operation of the three individual masks can, therefore, be expected to include most of the object pixels. The proposed method is applied to a number of video sequences. The simulation results demonstrate that the proposed method provides a mechanism for shadow removal that is superior to some of the recently proposed techniques without imparting an excessive computational cost.     

An efficient system for anomaly detection using deep learning classifier

In this paper, a deep learning-based anomaly detection (DLAD) system is proposed to improve the recognition problem in video processing. Our system achieves complete detection of abnormal events by involving the following significant proposed modules a Background Estimation (BE) Module, an Object Segmentation (OS) Module, a Feature Extraction (FE) Module, and an Activity Recognition (AR) Module. At first, we have presented a BE (Background Estimation) module that generated an accurate background in which two-phase model is generated to compute the background estimation. After a high-quality background is generated, the OS model is developed to extract the object from videos, and then, object tracking process is used to track the object through the overlapping detection scheme. From the tracked objects, the FE module is extracted for some useful features such as shape, wavelet, and histogram to the abnormal event detection. For the final step, the proposed AR module is classified as abnormal or normal event using the deep learning classifier. Experiments are performed on the USCD benchmark dataset of abnormal activities, and comparisons with the state-of-the-art methods validate the advantages of our algorithm. We can see that the proposed activity recognition system has outperformed by achieving better EER of 0.75 % when compared with the existing systems (20 %). Also, it shows that the proposed method achieves 85 % precision rate in the frame-level performance.     

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems

Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.     

An efficient full-wave method of moments analysis for RLSA antennas

Here a computationally efficient method of moments (MoM) formulation is presented for the full-wave analysis and design of radial line slot antennas (RLSAs). The method efficiency is based on an ad-hoc representation of the internal problem (electrically large circular cavity) Green's function, which is split into the sum of a parallel plate waveguide contribution and a rim effect contribution, expanded in terms of radial modes. The convergence rate of the proposed Green's function expansion is investigated both analytically and numerically with the aid of some examples. Furthermore, comparisons with a commercial general purpose MoM software were performed on a complete RLSA to demonstrate the accuracy and the efficiency of our approach.     

An efficient stiffness-insensitive method for transient analysis ofMarkov availability models

This paper proposes a hybrid approach (HybApp) for numerical transient solution of Markov availability models. It solves both stiff and nonstiff models using a heuristic that provides timely, inexpensive stiffness detection in the model. If the model is found stiff, then a stiff ordinary-differential-equation method is used to solve it from that point onwards, otherwise we continue to use uniformization. Numerical results show the advantages of HybApp     

一种卫星物联网上行干扰分析高效计算方法

低轨卫星物联网(IoT)作为空间信息网络的重要组成部分,由于其星座规模激增,卫星平台动态性强,终端数量多,在与其他通信系统进行干扰分析及协调时,存在干扰计算复杂度较高的问题。实际中,物联网终端的分布与地理和人口高度相关,但由于单颗卫星覆盖面积大,面临多种密度和部署终端,采用静态遍历计算的方式将带来运算量爆炸。本文从同频干扰产生的机理出发,以区域化发射场强作为密集物联网终端上行集总干扰的建模核心,并引入泊松点过程(PPP)分布模型对地面终端进行等效建模,使区域化的上行集总干扰仅与分布参数及发射参数有关,而与终端确切位置、数量均无关。仿真结果表明,本文方法与传统链路分析结果相比误差较小,对不同数量终端的拟合性能均较好,并能有效降低长时分析的计算复杂度,能够用于高效干扰分析与计算。     

An efficient algorithm for dynamic task offloading using cloudlets in mobile cloud computing

It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time.