Spectrum and Bit Error Rate (BER) performance of a coded π/2 phase shift Non-Square (NS) 8 Quadrature Amplitude Modulation (QAM) system are studied in this letter. The modulation process of this scheme removes all 180 phase shift between adjacent constellation points and contains inherent memory which can be treated as a type of inner coding in coded system. Simulation results show that this modulation scheme has much lower spectrum regrowth and better BER performance when passing through nonlinear channel compared with conventional mode.

Non-contiguous orthogonal frequency-division multiplexing (OFDM) is a promising technique for cognitive radio systems. The secondary users transmit on the selected subcarriers to avoid the frequencies being used by the primary users. However, the out-of-band power (OBP) of the OFDM-modulated tones induces interference to the primary users. Another major drawback of OFDM-based system is their high peak-to-average power ratio (PAPR). In this paper, algorithms are proposed to jointly reduce the OBP and the PAPR for non-contiguous OFDM based on the method of alternating projections onto convex sets. Several OFDM subcarriers are selected to accommodate the adjusting weights for OBP and PAPR reduction. The frequency-domain OFDM symbol is projected onto two convex sets that are defined according to the OBP requirements and the PAPR limits. Each projection iteration solves a convex optimization problem. The projection onto the set constrained by the OBP requirement can be calculated using an iterative algorithm which has low computational complexity. Simulation results show good performance of joint reduction of the OBP and the PAPR. The proposed algorithms converge quickly in a few iterations.

In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.

Depression is a prevalent mental disorder affecting a significant portion of the global population, leading to considerable disability and contributing to the overall burden of disease. Consequently, designing efficient and robust automated methods for depression detection has become imperative. Recently, deep learning methods, especially multimodal fusion methods, have been increasingly used in computer-aided depression detection. Importantly, individuals with depression and those without respond differently to various emotional stimuli, providing valuable information for detecting depression. Building on these observations, we propose an intra- and inter-emotional stimulus transformer-based fusion model to effectively extract depression-related features. The intra-emotional stimulus fusion framework aims to prioritize different modalities, capitalizing on their diversity and complementarity for depression detection. The inter-emotional stimulus model maps each emotional stimulus onto both invariant and specific subspaces using individual invariant and specific encoders. The emotional stimulus-invariant subspace facilitates efficient information sharing and integration across different emotional stimulus categories, while the emotional stimulus specific subspace seeks to enhance diversity and capture the distinct characteristics of individual emotional stimulus categories. Our proposed intra- and inter-emotional stimulus fusion model effectively integrates multimodal data under various emotional stimulus categories, providing a comprehensive representation that allows accurate task predictions in the context of depression detection. We evaluate the proposed model on the Chinese Soochow University students dataset, and the results outperform state-of-the-art models in terms of concordance correlation coefficient (CCC), root mean squared error (RMSE) and accuracy.

A new artificial fish swarm algorithm (AFSA) for solving the multiple knapsack problem (MKP) is introduced in this paper. In the proposed AFSA, artificial fish (AF) individuals are only allowed to search the region near constraint boundaries of the problem to be solved. For this purpose, several behaviors to be performed by AF individuals, including escaping behavior, randomly moving behavior, preying behavior and following behavior, were specially designed. Exhaustive experiments were implemented in order to investigate the proposed AFSA's performance. The results demonstrated the proposed AFSA has the ability of finding high-quality solutions with very fast speed, as compared with some other versions of AFSA based on different constraint-handling methods. This study is also meaningful for solving other constrained problems.

In this paper, we address the survivable routing problem with and without wavelength-continuity constraints by proposing a new Integer Linear Programming (ILP) algorithm, which is based on a simplified necessary and sufficient condition. Numerical results are given and discussed to show the efficiency of our algorithm and the impact of wavelength-continuity constraints.

This paper presents an artificial fish swarm algorithm (AFSA) to solve the multicast routing problem, which is abstracted as a Steiner tree problem in graphs. AFSA adopts a 0-1 encoding scheme to represent the artificial fish (AF), which are then subgraphs in the original graph. For evaluating each AF individual, we decode the subgraph into a Steiner tree. Based on the adopted representation of the AF, we design three AF behaviors: randomly moving, preying, and following. These behaviors are organized by a strategy that guides AF individuals to perform certain behaviors according to certain conditions and circumstances. In order to investigate the performance of our algorithm, we implement exhaustive simulation experiments. The results from the experiments indicate that the proposed algorithm outperforms other intelligence algorithms and can obtain the least-cost multicast routing tree in most cases.

An artificial fish swarm algorithm for solving symbolic regression problems is introduced in this paper. In the proposed AFSA, AF individuals represent candidate solutions, which are represented by the gene expression scheme in GEP. For evaluating AF individuals, a penalty-based fitness function, in which the node number of the parse tree is considered to be a constraint, was designed in order to obtain a solution expression that not only fits the given data well but is also compact. A number of important conceptions are defined, including distance, partners, congestion degree, and feature code. Based on the above concepts, we designed four behaviors, namely, randomly moving behavior, preying behavior, following behavior, and avoiding behavior, and present their respective formalized descriptions. The exhaustive simulation results demonstrate that the proposed algorithm can not only obtain a high-quality solution expression but also provides remarkable robustness and quick convergence.

In this paper, a CMOS Class-G supply modulation for polar power amplifiers with high average efficiency and low ripple noise is proposed. In the proposed Class-G supply modulation, the parallel supply modulations which are controlled by switch signals are utilized for low power and high power supplies to increase the average efficiency. A low dropout (LDO) is utilized to suppress the delta-modulated noise and provide a low ripple noise power supply. The proposed supply modulation has high efficiency at large output current as the conventional supply modulation, and it also has high efficiency and low ripple noise at the low output current. To verify the effectiveness of the proposed supply modulation, the proposed supply modulation was designed with 0.13 µm CMOS process. The simulation results show that the proposed supply modulation achieves a maximum efficiency of 85.1%. It achieves an average efficiency of 29.3% and a 7.1% improvement compared with the conventional supply modulations with Class-E power amplifier. The proposed supply modulation also shows an excellent spurious free dynamic range (SFDR) of -73 dBc for output envelope signal.

A balanced push-push frequency doubler has been demonstrated in 0.25-µm SOI (Silicon on Insulator) SiGe BiCMOS technology operating from 22 GHz to 29 GHz with high fundamental frequency suppression and high conversion gain. A series LC resonator circuit is connected in parallel with the differential outputs of the doubler core circuit. The LC resonator is effective to improve the fundamental frequency suppression. In addition, the LC resonator works as a matching circuit between the output of the doubler core and the input of the output buffer amplifier, which increases the conversion gain of the whole circuit. A measured fundamental frequency suppression of greater than 46 dBc is achieved at an input power of -10 dBm in the output frequency band of 22-29 GHz. Moreover, maximum fundamental frequency suppression of 66 dBc is achieved at an input frequency of 13 GHz and an input power of -10 dBm. The frequency doubler works at a supply voltage of 3.3 V.

A novel resonant circuit consisting of transformer-based switched variable inductors and switched accumulation MOS (AMOS) varactors is proposed to realize an ultrawide tuning range voltage-controlled-oscillator (VCO). The VCO IC is designed and fabricated using 0.11 µm CMOS technology and fully evaluated on-wafer. The VCO exhibits a frequency tuning range as high as 92.6% spanning from 1.20 GHz to 3.27 GHz at an operation voltage of 1.5 V. The measured phase noise of -120 dBc/Hz at 1 MHz offset from the 3.1 GHz carrier is obtained.

A broadband balanced frequency doubler has been demonstrated in 0.25-µm SOI SiGe BiCMOS technology to operate from 22 GHz to 30 GHz. The measured fundamental frequency suppression of greater than 30 dBc is achieved by an internal low pass LC filter. In addition, a pair of matching circuits in parallel with the LO inputs results in high suppression with low input drive power. Maximum measured conversion gain of -6 dB is obtained at the input drive power as low as -1 dBm. The results presented indicate that the proposed frequency doubler can operate in broadband and achieve high fundamental frequency suppression with low input drive power.