In recent years, wireless multimedia sensor networks (WMSNs) have emerged as a prominent technique for delivering multimedia information such as still images and videos. Being under the great spotlight of research communities, however, multimedia delivery over resource- constraint WMSNs poses great challenges, especially in terms of energy efficiency and quality-of-service (QoS) guarantees. In this paper, recent developments in techniques for designing highly energy-efficient and QoS-capable WMSNs are surveyed. We first study the unique characteristics and the relevantly imposed requirements of WMSNs. For each requirement we also summarize their existing solutions. Then we review recent research efforts on energy-efficient and QoS-aware communication protocols, including MAC protocols, with a focus on their prioritization and service differentiation mechanisms and disjoint multipath routing protocols.

Visible light communication (VLC) applied in an intelligent transportation system (ITS) has attracted growing attentions, but it also faces challenges, for example deep path loss and optical multi-path dispersion. In this work, we modelled an actual outdoor optical channel as a Rician channel and further proposed space-time block coding (STBC) orthogonal frequency-division multiplexing (OFDM) technology to reduce the influence of severe optical multi-path dispersion associated with such a mock channel for achieving the effective BER of 10⁻⁶ even at a low signal-to-noise ratio (SNR). In this case, the optical signals transmission distance can be extended as long as possible. Through the simulation results of STBC-OFDM and single-input-single-output (SISO) counterparts in bit error rate (BER) performance comparison, we can distinctly observe that the VLC–ITS system using STBC-OFDM technique can obtain a strongly improved BER performance due to multi-path dispersion alleviation.

The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.

A family of Nyquist-I pulses called sinc parametric linear combination pulse (SPLCP) is proposed. It is characterized by two novel design parameters that provide additional degrees of freedom to minimize the intercarrier interference (ICI) power due to frequency offset. Moreover, it reduces the high peak-to-average power ratio (PAPR) value in orthogonal frequency division multiplexing (OFDM) systems. Several Nyquist-I pulses were recently proposed to address the subject of high sensitivity to frequency offset and high PAPR in OFDM-based transmissions. In this paper, we investigate the performance of SPLCP in terms of ICI power, signal-to-interference ratio (SIR) power, bit error rate (BER), and PAPR. We additionally examine the behaviour of SPLCP with new design parameters for a certain roll-off factor, α. We compare the performance of SPLCP with other well-known pulses. Theoretical and simulation results show that the proposed SPLCP outperforms other existing pulses in terms of ICI power, SIR power, BER, and PAPR.

Quantum mechanics offers new opportunities for diverse information processing tasks in communication and computational networks. In the last two decades, the notion of quantum anonymity has been introduced in several networking tasks that provide an unconditional secrecy of identity for the communicating parties. In this article, we propose a quantum anonymous collision detection (QACD) protocol which detects not only the collision but also guarantees the anonymity in the case of multiple senders. We show that the QACD protocol serves as an important primitive for a quantum anonymous network that features tracelessness and resource efficiency. Furthermore, the security analysis shows that this protocol is robust against the adversary and malicious participants.