In this paper, we present a novel lightweight elliptic curve scalar multiplication architecture for random Weierstrass curves over prime field ${\mathbb{F}}_p$. The elliptic curve scalar multiplication is executed in Jacobian coordinates based on the Montgomery ladder algorithm with (X,Y)-only common Z coordinate arithmetic. At the finite field operation level, the adder-based modular multiplier and modular divider are optimized by the pre-calculation method to reduce the critical path while maintaining low resource consumption. At the group operation level, the point addition and point doubling methods in (X,Y)-only common Z coordinate arithmetic are modified to improve computation parallelism. A compact scheduling method is presented to improve the architecture’s performance, which includes appropriate scheduling of finite field operations and specific register connections. Compared with existing works, our design is implemented on the FPGA platform without using DSPs or BRAMs for higher portability. It utilizes 6.4~6.5k slices in Kintex-7, Virtex-7, and ZYNQ FPGA and executes an elliptic curve scalar multiplication for a field size of 256-bit in 1.73 ms, 1.70 ms, and 1.80 ms, respectively. Additionally, our design is resistant to timing attacks, simple power analysis attacks, and safe-error attacks. This architecture outperforms most state-of-the-art lightweight designs in terms of area-time products.

Speech Emotion Recognition (SER) plays a significant role in the field of Human–Computer Interaction (HCI) with a wide range of applications. However, there are still some issues in practical application. One of the issues is the difference between emotional expression amongst various individuals, and another is that some indistinguishable emotions may reduce the stability of the SER system. In this paper, we propose a multi-layer hybrid fuzzy support vector machine (MLHF-SVM) model, which includes three layers: feature extraction layer, pre-classification layer, and classification layer. The MLHF-SVM model solves the above-mentioned issues by fuzzy c-means (FCM) based on identification information of human and multi-layer SVM classifiers, respectively. In addition, to overcome the weakness that FCM tends to fall into local minima, an improved natural exponential inertia weight particle swarm optimization (IEPSO) algorithm is proposed and integrated with fuzzy c-means for optimization. Moreover, in the feature extraction layer, non-personalized features and personalized features are combined to improve accuracy. In order to verify the effectiveness of the proposed model, all emotions in three popular datasets are used for simulation. The results show that this model can effectively improve the success rate of classification and the maximum value of a single emotion recognition rate is 97.67% on the EmoDB dataset.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is emerging as a promising technology by achieving full-space coverage and further improving system performance. However, most existing works adopted an independent phase-shift model, which is high-cost and may be difficult to achieve in realistic wideband systems. Consequently, a coupled phase-shift STAR-RIS enhanced downlink multi-user multiple-input single-output orthogonal frequency division multiplexing system is investigated for both unicast and broadcast communications in this paper. We aim to maximize the average sum-rate (ASR) for all subcarriers by jointly optimizing the precoding matrices and the reflecting and transmitting coefficients (RTCs). Specifically, a block coordinate descent algorithm is proposed to iteratively design each block of a multiblock problem reformulated by the original one. The precoding matrices are optimized by the Lagrangian multiplier method for low computational complexity. For the RTCs, an element-based alternating optimization algorithm is proposed to optimize the coupled phase-shift and amplitude coefficients. Simulation results validate the effectiveness of the proposed algorithm by comparing the ASR with that of other benchmarks. Moreover, its performance closely approaches the upper bound under various practical user proportion scenarios on both sides of the STAR-RIS.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) represents a novel technology that extends communication coverage to the entire space, compared to the conventional reconfigurable intelligent surface (RIS) covering only half space. However, previous studies mostly focused solely on best-effort services, potentially resulting in lower communication quality for users with poor channel conditions or even communication failure. Consequently, the fairness among users is investigated in the STAR-RIS-assisted multi-input-multi-output system to improve the system performance. To minimize the maximum average mean-square error of each user, a fair beamforming design is utilized to jointly optimize the transmit precoding matrices, reflecting and transmitting coefficients (RTCs), as well as receive combining matrices. To tackle this problem, we first decompose it into three tractable subproblems and then utilize an alternating-optimization algorithm to optimize each one alternately. For the transmit precoding and receive combining matrices, each matrices are obtained by transforming the nonconvex subproblem into a second-order cone programming problem. For the RTCs, considering a low-cost coupled phase shift STAR-RIS model, an element-wise two-stage search algorithm is proposed to optimize the phase shift and amplitude for both energy splitting and mode switching STAR-RIS protocols. Also, the proposed algorithms are extended to the robust beamforming design under imperfect channel state information. Simulation results demonstrate that the proposed algorithm outperforms the other benchmark schemes in the STAR-RIS-assisted MIMO system. Moreover, its performance is closest to the lower bound under various differences in channel conditions among users.