Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition‐based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition‐based learning is introduced to promote the population diversity. Dimension‐by‐dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

We continue the study of Mastrovito form of Karatsuba (MK) multipliers under the shifted polynomial basis (SPB). An MK multiplier utilizes Karatsuba algorithm and Mastrovito approach to optimize polynomial multiplication and modular reduction, which lead to a better space and time tradeoff for all trinomials. Based on this work, we make two types of contributions: 1) We derive a new modular reduction formulation for constructing Mastrovito matrix associated with special types of pentanomials, i.e., Types I, II, and Type C.1 pentanomial. Through related formulations, we demonstrate that Type I pentanomial is less efficient than Type II because of a more complicated modular reduction under the same SPB; conversely, Type C.1 pentanomial is as good as Type II under generalized polynomial basis (GPB). 2) We introduce a new MK multiplier for Type II pentanomial. It is shown that our proposal is only one TX slower than the fastest quadratic multipliers for Type II pentanomial, but its space complexity is roughly 3/4 of those schemes. To the best of our knowledge, it is the first time for hybrid multiplier to achieve such a time delay bound.

Searchable public key encryption- (SPE-) supporting keyword search plays an important role in cloud computing for data confidentiality. The current SPE scheme mainly supports conjunctive or disjunctive keywords search which belongs to very basic query operations. In this paper, we propose an efficient and secure SPE scheme that supports Boolean keywords search, which is more advanced than the conjunctive and disjunctive keywords search. We first develop a keyword conversion method, which can change the index and Boolean keywords query into a group of vectors. Then, through applying a technique so-called dual pairing vector space to encrypt the obtained vectors, we propose a concrete scheme proven to be secure under chosen keyword attack. Finally, we put forward a detailed theoretical and experimental analysis to demonstrate the efficiency of our scheme.

Complex-valued Magnetic Resonance Imaging (MRI) is widely used in clinical diagnosis. The magnitude images are mainly used for structure visualization, and the phase images reveal tissue properties, such as magnetic susceptibility and fluid flow information. While MRI is slow, compressed sensing (CS) can be used to reconstruct the accelerated acquisitions. Current CS-based MRI reconstruction algorithms mostly focus on magnitude image recovery, with less attention to the recovery of phase images. In this paper, we propose a novel CS algorithm to simultaneously recover the magnitude and phase MR images based on the sparsity of the trigonometric function. The CS method requires a sparse representation of the original images, and it is observed the trigonometric functions of phase images in the Wavelet domain promote sparsity. Therefore, rather than transforming the phase images directly into the Wavelet domain, we calculate the sine and cosine of the phase images whose Wavelet transforms are set as the L1-norm regularization term. The combination of the dual trigonometric functions captures a unique, faithful four-quadrant phase information, which also improves the reconstructed magnitude images through an alternating optimization procedure. Reconstructions of simulated images and in vivo images are studied, and both show the superiority of the proposed method over compared phase recovery algorithms.

Food image recognition is a crucial initial step for food detection, nutritional analysis, and dietary recommendations. However, it is highly challenging due to the diverse appearance of food, which includes variations in color, texture, and presentation. We believe that effective food image recognition requires exploiting the characteristics of food images, with color playing a dominant role in feature extraction. This paper proposes a novel color-cluster attention network model for food recognition, which integrates Kohonen networks-based clustering methods with iterative feature refinement. First, the model leverages Kohonen networks-based clustering methods to effectively group similar features, which helps in capturing the complex compositions of food ingredients. Then, we apply an iterative strategy to the feature extraction module to gradually capture finer image details, and utilize a self-attention mechanism for weighting to further enhance feature representation. Extensive experiments conducted on various food image datasets, including ETH Food-101, Food11, Food new and CAFD to evaluate the effectiveness of the proposed model. The results show that the proposed model significantly improves the recognition accuracy and validate the generalization ability of the method on multiple food datasets.