Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.

In the next generation wireless Internet of Things (IoT) networks empowered by modern communication technology, non-orthogonal multiple access (NOMA) and faster-than-Nyquist (FTN) signaling are purportedly two enabling technologies that enhance spectral efficiency (SE) without requiring additional spectrum resources. In addition, simultaneous wireless information and power transfer (SWIPT) technology enables IoT sensors and devices to harvest energy from radio frequency (RF) signals, effectively mitigating power supply limitations. This article proposes and investigates a novel SWIPT-NOMA system based on FTN technology, referred to as FTN-assisted SWIPT-NOMA, for IoT relay networks over Rayleigh fading channels. We provide a comprehensive analysis of the ergodic capacity and achievable rate of the FTN-assisted SWIPT-NOMA system applied in IoT relay networks. Specifically, we explore two distinct relaying architectures geared towards augmenting SE and energy utilization, i.e., power-splitting (PS) and time-switching (TS). We derive approximated expressions for the ergodic capacity and analyze high SNR slopes for sensor users in both architectures. Simulation results show that the ergodic capacity of the proposed system surpasses that of the conventional Nyquist-SWIPT-NOMA system, with greater capacity improvements as the FTN acceleration factor τ decreases. This highlights the substantial potential of FTN-assisted SWIPT-NOMA systems in enhancing the performance of IoT relay networks, particularly with respect to SE.

To enable highly reliable and spectrum-efficient vehicle-to-everything (V2X) communications under conditions with severe Doppler effects and rapidly time-varying channels, we propose a novel faster-than-Nyquist orthogonal time frequency space (FTN-OTFS) modulation scheme. In this scheme, FTN signaling is integrated with spectrally efficient frequency division multiplexing (SEFDM) within the OTFS framework, enabling a higher symbol-transmission density within a fixed time–frequency resource block and thus enhancing spectral efficiency without increasing the occupied bandwidth. An analytical input–output model is derived in both the delay–Doppler and time–frequency domains. To further enhance numerical stability, an improved detection algorithm called adaptive damping log-domain message-passing (ADL-MP) is developed for the proposed scheme. Simulation results demonstrate that the proposed scheme achieves robust and reliable performance in high-mobility scenarios and that the proposed algorithm consistently outperforms conventional methods in terms of bit error rate (BER) under both the extended vehicular A (EVA) model and the high-speed train (HST) scenario, confirming its effectiveness and superiority for V2X communications.

Intelligent Reflective Surface (IRS)-aided Multiple-Input Multiple-Output (MIMO) systems have emerged as a promising solution to enhance spectral and energy efficiency in future wireless communications. However, accurate channel estimation remains a key challenge due to the passive nature and high dimensionality of IRS channels. This paper proposes a lightweight hybrid framework for cascaded channel estimation by combining a physics-based Bilinear Alternating Least Squares (BALS) algorithm with a deep neural network named ConvTrans-ResNet. The network integrates convolutional embeddings and Transformer modules within a residual learning architecture to exploit both local and global spatial features effectively while ensuring training stability. A series of ablation studies is conducted to optimize architectural components, resulting in a compact configuration with low parameter count and computational complexity. Extensive simulations demonstrate that the proposed method significantly outperforms state-of-the-art neural models such as HA02, ReEsNet, and InterpResNet across a wide range of SNR levels and IRS element sizes in terms of the Normalized Mean Squared Error (NMSE). Compared to existing solutions, our method achieves better estimation accuracy with improved efficiency, making it suitable for practical deployment in IRS-aided systems.

A phase-shifted dual-bridge series resonant DC-DC converter (DBSRC) is a competitive candidate for applications of an energy storage system. At the request of a step-load-change command such as the start-up and power-level change, the converter may suffer from large-amplitude transient oscillations due to improper transient modulation. Furthermore, the DC bias current and overshoot current/voltage in the resonant tank and transformer caused by oscillations may result in transformer saturation and poor dynamic performance. To solve these problems, two fast transient modulation (FTM) methods are proposed in this paper. First, based on the steady-state analysis of the converter with phase-shift control, the current and voltage trajectory of the resonant capacitor can be obtained. Then, the detailed principles of two FTM methods are explained for achieving a smooth transition. Through the adjustment of the durations of the adjacent switching intervals temporarily, the transient trajectory can be predicted and is expected to match the destination trajectory within one switching period. Consequently, the proposed FTM methods enable the converter to move from one steady state to another instantly and the step-load-change transition can be an overshoot-free procedure. Finally, both simulation and experimental tests prove that the two modulation methods can effectively eliminate DC bias current and overshoot current/voltage in the DBSRC transient process and obtain a fast transient response.