Tag collision has a negative impact on the performance of RFID systems. In this letter, we propose an algorithm termed anti-collision protocol based on improved collision detection (ACP-ICD). In this protocol, dual prefixes matching and collision bit detection technique are employed to reduce the number of queries and promptly identify tags. According to the dual prefixes matching method and collision bit detection in the process of collision arbitration, idle slots are eliminated. Moreover, the reader makes full use of collision to improve identification efficiency. Both analytical and simulation results are presented to show that the performance of ACP-ICD outperforms existing anti-collision algorithms.

This paper discusses how to compute the parametric sensitivity function in continuous-time Markov chains (CTMC). The sensitivity function is the first derivative of the steady-state probability vector regarding a CTMC parameter. Since the sensitivity function is given as a solution of linear equations with a sparse matrix, several linear equation solvers are available to obtain it. In this paper, we consider Jacobi and successive-over relaxation as variants of the Gauss-Seidel algorithm. In addition, we develop an algorithm based on the Takahashi method for the sensitivity function. In numerical experiments, we comprehensively evaluate the performance of these algorithms from the viewpoint of computation time and accuracy.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

With the development of Internet technology, the demand for signing electronic contracts has been greatly increased. The electronic contract generated by the participants in an online way enjoys the same legal effect as paper contract. The fairness is the key issue in jointly signing electronic contracts by the involved participants, so that all participants can either get the same copy of the contract or nothing. Most existing solutions only focus on the fairness of electronic contract generation between two participants, where the digital signature can effectively guarantee the fairness of the exchange of electronic contracts and becomes the conventional technology in designing the contract signing protocol. In this paper, an efficient blockchain-based multi-party electronic contract signing (MECS) protocol is presented, which not only offers the fairness of electronic contract generation for multiple participants, but also allows each participant to aggregate validate the signed copy of others. Security analysis shows that the proposed MECS protocol enjoys unforgeability, non-repudiation and fairness of electronic contracts, and performance analysis demonstrates the high efficiency of our construction.