Multidimensional Systems and Signal Processing最新文献

This paper presents a wideband robust beamforming algorithm based on a complex quantized convolutional neural network (CQCNN) for solving the steering vector (SV) mismatch problem, named as CQCNN-SV algorithm. Firstly, the CQCNN is constructed by the complex convolution layers, quantization assistance layers, and normalization layers, respectively. Specially, the network channel filtering threshold function is used to construct the quantization assistance layer with the functions of network weight pruning. The CQCNN structure is suitable for wideband beamforming in space–time two-dimensional signal processing, which can improve the feature extraction ability and convergence speed of complex-valued data. Subsequently, the mismatched desired signal SV is corrected by solving the quadratic programming problem, and the corrected SV is treated as the training label. Finally, the space–time two-dimensional covariance matrix and the training label are fed into the CQCNN model. The wideband beamforming weight vector in the space–time antenna structure is given by the desired signal SV, which is predicted by the well-trained CQCNN. Theoretical analysis and simulation experiments show that the proposed algorithm not only has good real-time performance but also has stable system output performance.

This article investigates the problem of removing noise from multi-modal medical images to ensure efficient Medical Image Fusion (MIF). The proposed MIF achieves optimal results with a novel hybrid image fusion scheme. This scheme is achieved with an improved performance of the Artificial Rabbits Optimization (ARO) algorithm and a novel cascaded combination of filters. The exploring mechanism of the classical ARO algorithm is enriched by incorporating the approaches adopted in Differential Evolution and thus termed Differential Evolution-based Artificial Rabbits Optimization (DEARO). The effectiveness of the novel DEARO algorithm is proven through the testing of the CEC 2017 benchmark functions and it is noticed that the proposed approach offers superior solutions than existing optimization algorithms. Ten image fusion quality evaluation metrics are compared to demonstrate the performance of the proposed approach. Considering Mutual Information (MI), the proposed method exhibits (40%) average improvements in the fusion of clean images. Similarly, (50%), (36%), and (21%) improvements are noticed in MI values when both the modalities of source images are contaminated with Gaussian, Salt & Pepper, and Speckle noises of variance 0.1. The qualitative evaluation of the fused image shows the advancement of the proposed scheme in multi-modal MIF compared to the contemporary approaches.

Terahertz imaging presents immense potential across many fields but the affordability of multiple-pixel imaging equipment remains a challenge for many researchers. To address this, the adoption of single-pixel imaging emerges as a lower-cost option, however, the data acquisition process necessary for reconstructing images is time-intensive. Compressive Sensing, which allows for generation of images using a reduced number of measurements than Nyquist's theorem demands, presents a promising solution but long processing times are still issue particularly large-sized images. Our proposed solution to this issue involves using caustic lens effect induced by perturbations in a ripple tank as a sampling mask. The dynamic nature of the ripple tank introduces randomness into the sampling process and this reduces measurement time by exploiting the inherent sparsity of THz band signals. This work employed Convolutional Neural Network to perform target classification based on the distinct signal patterns acquired through the caustic lens mask. The proposed classifier achieved 99.22% accuracy rate in distinguishing targets shaped like Latin letters. The controlled randomness introduced by the caustic lens mask is believed to play a crucial role in achieving this high accuracy by mitigating overfitting, a common challenge in machine learning.

An efficient 2-D Finite Impulse Response (FIR) filter is designed using modified McClellan transformations with optimized coefficients. The P3 transformation is considered to attain sharp circular symmetry filters to reduce the complexity of the architecture of the 2-D FIR filter. The filter coefficients are represented in Canonical Signed Digit (CSD) space to construct the filter architecture by multiplierless design. The CSD representation is optimized using the Cuckoo Search Algorithm (CSA) with fitness function Mean Square Error (MSE). Further, a Fully Direct (FD) type architecture of a 2-D FIR filter is implemented according to the obtained CSD-based coefficients for the length of (Ntimes N =11 times 11). Each row filter structure is realized and explored. All the hardware structures of row filters were realized and integrated using Verilog HDL and synthesized by Genus tools provided by the CADENCE Vendor in a 45 nm CMOS generic library. The area, delay, and power reports are generated by this synthesis tool and compared with the existing 2-D FIR filter architectures. The area, power, and delay values of the proposed filter architecture are decreased by 28.9%, 49.59%, and 36.02%, respectively to the conventional filter architecture. The Power-Delay-Product (PDP) and Area-Delay-Product (ADP) values of the proposed filter architecture are reduced by a minimum of 2.14 and 1.96 times, and a maximum of 4.31 and 66 times to the existing filter architectures respectively.

This paper is to propose a new definition of two-dimensional (2D) Wigner distribution (2D-WD) and two-dimensional ambiguity function (2D-AF) associated with two-dimensional nonseparable linear canonical transform (2D-NS-LCT), namely 2D-NLCWD and 2D-NLCAF. This allows for several consequences of the basic properties of the proposed distributions such as the shift properties, the conjugation symmetry property, the marginal properties, the Moyal formula, and the relationships with the two-dimensional short-time Fourier transform (2D-STFT). Furthermore, we point out the usefulness and efficacy of newly defined distributions for detecting two-dimensional linear frequency-modulated (2D-LFM) signals.

There are many observer design approaches that have been developed to estimate the state of a linear time delay system. This paper focuses on the observer design problem for two-dimensional (2-D) continuous systems with delays proposed by Roesser’s state space model. A new sufficient condition for 2-D state observer design of 2-D continuous-time systems with delays is developed. The key point is that the Lyapunov theory that is used here allows us to solve the problem using the technique of linear matrix inequalities, which is used to establish the 2-D state observers with delays. Finally, to illustrate the effectiveness of the proposed methodology, a numerical example is provided.