75results about How to "Computational time complexity is low" patented technology

The invention claims a user incentive method in a mobile crowd-sensing network based on reverse auction. The method utilizes a reverse auction framework to motivate users to participate in location-related sensing activities, and considers random exit situations in the process of user sensing. The scheme aims to increase the enthusiasm of users to participate in the sensing activities with the goal of maximizing user utility under the premise of a feasible budget. First, a task-centric approach is used to select users as bid winners of sensing tasks to ensure high task coverage. Secondly, according to the completion of the tasks of the bid winners, the users are paid according to a time proportion sharing rule to ensure the authenticity of the incentive. The user incentive method in the mobile crowd-sensing network based on reverse auction has the characteristics of computational validity, individual rationality, budget balance, authenticity and the like. The user incentive method of the invention can enable the users to obtain maximum utility, motivate the users to participate in the tasks, and improve the enthusiasm of the users to participate.

The invention discloses a mutual coupling correction-based radar array adaptive beamforming method. The method includes the following steps that: a mathematical model of the receiving of Q different direction signals by a uniform circular array of M array elements is established, an expression X<^>(t) of the receiving of Q different direction signals containing noises by the uniform circular array of M array elements and K received signal samples are obtained, and an interference and noise sampling covariance matrix R <~> of a sample matrix XK is calculated; the output power Pout (theta0, phi) of desired signals to be detected of which the incident direction is (theta0, phi) is calculated, C is constructed, and C and the output power Pout (theta0, phi) of the desired signals to be detected of which the incident direction is (theta0, phi) are adopted as optimization equations of a quantity to be solved, and a final optimization equation is obtained; it is determined that the product of C and the ideal steering vector alpha (theta0, phi) of the desired signals to be detected of which the incident direction is (theta0, phi) is equivalent to the product of an M*L-dimensional matrix T corresponding to M elements included in the alpha (theta0, phi) and a mutual coupling vector, and it is determined that the mutual coupling vector is composed of the first L elements in the first row in C; and the solution of c and the solution of C are calculated; and an optimization function is constructed, and a mutual coupling correction-based adaptive beamformer weight vector is calculated.