With the development of modern cities, people's traffic behaviors are on an ever-more increase. However, urban traffic is often congested due to bad road conditions or unreasonable road planning. To predict the trajectory of urban taxis, the taxi trajectories data are excavated based on the analysis of urban taxi behaviors. Then, the STTM (Spatio-Temporal Trajectory Model) is proposed using the LSTM (Long Short-Term Memory) and network residual. Meanwhile, the taxi is used for the urban road traffic perception and extraction, so that the taxi sensor and road traffic information can cooperate to construct a model for urban scale calculation. Afterward, the influence of the same model on the travel time predictions is compared for different urban roads. The results show that, based on the proposed STTM, the MRPE (Mean Relative Percentage Error) of the predicted value is 6.126%, the MAE (Mean Absolute Error) is 72.416 seconds, the MRE (Mean Relative Error) is 7.022%, the RMSE (Root Mean Square Error) is 293.977 seconds, and the coefficient of determination R2 is 0.884. This indicates that the model results have high goodness of fit, so it is a successful case of the application of urban computing in taxi trajectory prediction. Overall, the taxi ID (Identifier) and weather conditions have a great influence on the prediction results of urban taxi trajectory, and the STTM has a more obvious effect on improving the accuracy of travel time prediction for urban roads.
This paper considers a novel cooperative design for radar-communication spectral sharing system with multiple-input multiple-output (MIMO) structure based on mutual information optimization. We present a new spectral sharing framework aiming at maximizing the radar mutual information. Specifically, the transmit sequences including radar waveform and communication codebook are designed simultaneously based on radar and communication system mutual information optimization. Subsequently, an iterative procedure is devised to maximize the radar mutual information under the constraints including the transmit power, the constant modulus requirement for radar waveform, and the communication system mutual information performance requirement constraint. The proposed approach exploits the alternating optimization based on the Minorization-Maximization (MM) framework, Lagrange algorithm, and the alternating direction method of multipliers (ADMM) techniques to decompose the original design into two simpler subproblems with a closed-form solution. Simulation results are presented to demonstrate the advantage of the proposed schemes.
A regional fault-tolerant restoration mechanism based on SDN is proposed for fiber communication networks. Simulation results show that this mechanism has shorter recovery time and lower restoration failure rate than other mechanism because of avoiding the occurrence of regional faults.
Measurement of linear frequency modulation (LFM) signal is significant for radar, communication, and electronic reconnaissance fields. An LFM signal is a wideband signal whose frequency varies linearly with time, and traditional measurement methods require very high sampling rates and heavy processing to estimate parameters of the LFM signal. In this article, we propose a multichannel cooperative sampling (MCS) system based on the finite rate of innovation (FRI) theory to sample and estimate the parameters of the real-valued LFM pulse sequence (LFMPS). The MCS system consists of three parts: the autocorrelation sampling structure (ACSS), the time-delayed ACSS, and the quadrature time-staggered sampling structure (QTSS). These three parts can sample the LFMPS with sub-Nyquist sampling rate, and using the subspace method in time or frequency domain, the discontinuity locations (DLs), chirp rates, and initial frequencies (IFs) of the LFMPS can be estimated with the sub-Nyquist samples, respectively. The sampling rate of the MCS system is determined by the rate of innovation of the LFMPS, instead of signal bandwidth (BW). The minimum number of samples required for parameter estimation is proven by theoretical analysis. In addition, a nuclear norm denoising algorithm is proposed based on the low-rank property of the signal subspace, which significantly improved the performance of the measurement system in the noise environment. Simulation and hardware experimental results demonstrate the effectiveness and robustness of the proposed method.
In this paper, a sub-Nyquist sampling system for multiband signals is presented, which can perform accurate spectrum sensing and reconstruction. The system is based on PXI bus and virtual instrument technology, which make the proposed system able to be adjusted flexibly for different input signals. Compressive sensing (CS) theory and modulated wideband converter (MWC) are adopted in the system. The detailed design process of the system is given in the paper. The extension method of observation matrix and the reconstruction algorithms are also explained in this paper. Experimental results show that the proposed system can sample the multiband signal at an extreme low frequency and recover the signal spectrum effectively.
The hierarchical architecture of an Enterprise Service Bus (ESB) based service composition environment, Synchro ESB, is proposed in this paper. Synchro ESB is a rapid service application development platform grounded on the infrastructure of JBI specification-compliant distributed ESB. By introducing ultra server, Synchro ESB enables centralized management of the ESB environment and BPM applications. Business process in ESB environment can be modeled with the formal method based on QPi calculus. Some important design and implementation considerations for the distributed ESB and management tools are also discussed. Application tests of the prototype system demonstrate Synchro ESB's effectiveness. Compared with traditional techniques and tools, Synchro ESB delivers better flexibility and scalability meanwhile stays compatible with legacy systems, thus reduces EAI application development and maintenance costs to a greater extent.
Long test application time is an important problem in system-on-a-chip (SOC) design, and test scheduling is the main way to solve this problem. It is equivalent to the NP-complete 2-D bin-packing problem, each core test is represented by a rectangle with height equal to the TAM width and width equal to the test time. In this paper, we present a time-divided of IP test to solve SOC test scheduling problems. The main idea of the solution is to test the IP into several time periods, which makes the solution more flexible. Meanwhile, we use cross-entropy method and B*-Tree based floor planning technique to solve the test scheduling problem. Experimental results on ITC'02 SOC test benchmarks indicate that our method can provide shorter test time compared with recent works.
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