Search

In this paper, a neurodynamic approach for image restoration is proposed. Image restoration is a process of estimating original images from blurred and/or noisy images. It can be considered as a mapping problem that can be solved by neural networks. Echo state network (ESN) is a recurrent neural network with a simplified training process, which is adopted to estimate the original images in this paper. The parameter selection is important to the performance of the ESN. Thus, the pigeon-inspired optimization (PIO) approach is employed in the training process of the ESN to obtain desired parameters. Moreover, the orthogonal design strategy is utilized in the initialization of PIO to improve the diversity of individuals. The proposed method is tested on several deteriorated images with different sorts and levels of blur and/or noise. Results obtained by the improved ESN are compared with those obtained by several state-of-the-art methods. It is verified experimentally that better image restorations can be obtained for different blurred and/or noisy instances with the proposed neurodynamic method. In addition, the performance of the orthogonal PIO algorithm is compared with that of several existing bioinspired optimization algorithms to confirm its superiority. [ABSTRACT FROM AUTHOR]

Multiple Signal Classification (MUSIC) is a high-performance Direction of Arrival (DOA) estimation algorithm, which has been widely used. The algorithm needs to calculate the covariance matrix, eigenvalue decomposition and spectral peak search. In the paper, the hardware structure of the existing Jacobi algorithm for Hermitian matrices is proposed. On this basis, a novel hardware acceleration of the MUSIC algorithm for sparse arrays and uniform linear arrays is proposed, and the sparse array is a nested array. There are two designs, Design 1 supports 1~10 nested array elements or 1~32 uniform linear array elements, distinguishes 1~32 sources, configures snapshots 1~2048, and the maximum number of iterations and iteration accuracy of the complex Jacobi algorithm. Design 2 only needs $101.8~\mu $ s to complete a DOA estimation when the number of array elements is 8, the number of sources is 1, and the snapshots is 128. In more detail, the Root Mean Squared Error (RMSE) of both can reach 0.03°. The logic resources on the Zynq-7000 development board are 14,761 and 28,305 Look-Up Tables (LUTs), respectively. [ABSTRACT FROM AUTHOR]

• The expression of the Synthetic Augmented Cross Array (SACA) is derived, which utilizes the concept of 2D array motions. • The proposed SACA structure can achieve dramatically high uniform DOFs with only a few physical sensors. • The generalization of SACA named as Improved Synthetic Augmented Cross Array (ISACA) is proposed. • The ISACA can achieve larger DOFs than SACA and further reduce the mutual coupling effect. • The properties of both SACA and ISACA are analyzed and simulations verify the superiority. Array motion can efficiently enhance the achievable number of degrees-of-freedom (DOFs) by the virtue of filling the holes in the difference co-array. In this paper, the concept of synthetic array based on array motion is extended to two dimensional cases. A new structured sparse planar array on a moving platform named as Synthetic Augmented Cross Array (SACA) is proposed. The proposed array yields a co-array with much larger virtual uniform rectangular array in it. Closed-form expressions of DOFs with different moving trails are also delineated. Then by further improving the DOFs of SACA, a series of generalized configurations named as Improved Synthetic Augmented Cross Array (ISACA) are proposed. With the same number of sensors, it can achieve larger DOFs than SACA. Numerical results are provided for performance comparison and validations of analysis. [ABSTRACT FROM AUTHOR]

• Hightlights • The identity of the decryptor in the inner product encryption can be specified precisely. • The identity has a hierarchical structure and supports delegating secret key. • The security is proven under the standard model and not the RO model, so it is more practical. • Theoretical and empirical analysis shows that the program is efficient. Inner product functional encryption (IPFE), as a novel cryptographic primitive, can be classified into public-key IPFE (PK-IPFE) and secret-key IPFE (SK-IPFE). PK-IPFE has the following functions: an encryptor generates the ciphertext by encrypting a vector x → , and a decryptor decrypts the ciphertext and obtains the inner product 〈 x → , y → 〉 if it holds the secret key related to vector y → . The general IPFE cannot describe the hierarchical structure. The superior cannot use the general IPFE to issue the delegating keys for the subordinates, which complicates key management. We first propose a new primitive, hierarchical identity-based PK-IPFE (HID-PK-IPFE). HID-PK-IPFE has the following features. First, the identity of the decryptor can be specified when encrypting. Second, the identity of the recipient has a hierarchical structure. In the hierarchical tree, the upper-level users can generate the private key of the lower-level users; that is, our scheme has a delegation function. We formalize the selective chosen plaintext attack model of HID-PK-IPFE, propose the scheme in the standard model, and prove the security of the scheme on the basis of d -DBDHE assumption. We conducted two sets of simulations. We set the maximum depth of the identity hierarchy to 10, and increased the vector length from 10 to 15. We then listed the time spent by each algorithm in these two ranges. The simulation results show that the running time of each algorithm of our HID-PK-IPFE is completely acceptable in actual application scenarios. [ABSTRACT FROM AUTHOR]