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Communication and sensing integrated networks (CSINs) refer to the ability of physical digital space perception and ubiquitous intelligent communication at the same time. These networks realize the perception and cooperative communication of multidimensional resources through the cooperative work of communication and sensing resources and have the ability of intelligent interaction and processing of new information flow. First, this study proposes the technical architecture of an enhanced CSIN (E-HFWN), studies its key technologies and performance indicators, and explains the air interface technology, including frame structure design, carrier aggregation, channel detection, physical skyline mapping, beamforming and management, resource allocation and scheduling. In the resource allocation scheme, an actor-critic reinforcement learning (RL) framework is used to divide the wireless resources. The goal is to maximize the amount of mutual information (MI) and minimize the end-to-end delay of the sensing terminal. Then, the performance of the E-HFWN is tested, including numerical simulation of wireless resource management, system peak rate, capacity, end-to-end delay and communication perception waveform sidelobe ratio. Finally, from the results of the E-HFWN index test, the E-HFWN is further enhanced on the basis of 5G mmWave. The enhanced sensing function can provide a priori information for the optimal and rapid scheduling of distributed computing power and provide richer data sources for artificial intelligence (AI) services and applications to enhance the robustness of the training model. The E-HFWN can contribute to the development of technologies related to 6G synaesthesia computing integrated networks, promote the consensus between academia and industry.

This paper presents a novel neural model of insect’s visual perception paradigm to address a challenging problem on detection of looming motion, particularly in extremely low-contrast, and highly variable natural scenes. Current looming detection models are greatly affected by visual contrast between moving target and cluttered background lacking robust and low-cost solutions. Considering the anatomical and physiological homology between preliminary visual systems of different insect species, this gap can be significantly reduced by coordinating motion and contrast neural processing mechanisms. The proposed model draws lessons from research progress in insect neuroscience, articulates a neural network hierarchy based upon ON/OFF channels encoding motion and contrast signals in four parallel pathways. Specifically, the two ON/OFF motion pathways react to successively expanding ON–ON and OFF–OFF edges through spatial–temporal interactions between polarity excitations and inhibitions. To formulate contrast neural computation, the instantaneous feedback normalization of preliminary motion received at starting cells of ON/OFF channels works effectively to suppress time-varying signals delivered into the ON/OFF motion pathways. Besides, another two ON/OFF contrast pathways are dedicated to neutralize high-contrast polarity optic flows when converging with motion signals. To corroborate the proposed method, we carried out systematic experiments with thousands of looming-square motions at varied grey scales, embedded in different natural moving backgrounds. The model response achieves remarkably lower variance and peaks more smoothly to looming motions in different natural scenarios, a significant enhancement upon previous works. Such robustness can be maintained against extremely low-contrast looming motion against cluttered backgrounds. The results demonstrate a parsimonious solution to stabilize looming detection against high input variability, analogous to insect’s capability.

Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona–Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.

At present, the planning of transmission lines mainly relies on human decision-making and lacks intelligence. This paper combines the advantages of GIS in processing spatial data with the advantages of genetic algorithm to explore the optimization method of transmission line planning. The combination of GIS and genetic algorithm can minimize the interference of human factors and quickly solve the path planning problem of transmission lines. According to the theoretical model of genetic algorithm, this study constructs the transmission line optimization model based on genetic algorithm, and realizes the Add-ins plug-in development of the transmission line planning model based on genetic algorithm with the help of C # language. Taking 500 kV overhead transmission line about 150 km from Jiantang Substation (starting point) in Shangri-La County to Tai’ an Substation (ending point) in Lijiang as an example, two groups of experiments are designed under the conditions of considering traffic single factor and comprehensive multi-factor respectively. It is obtained that the path optimization effect of genetic algorithm is the best under the condition of comprehensive multi-factor, which proves the rationality and superiority of the model constructed in this study.

Previous learning-based methods only employ clear images to train the dehazing network, but some useful information such as hazy images, media transmission maps and atmospheric light values in datasets were ignored. Here, we propose a local feature residual network (LFR-Net) for single image dehazing, which is aimed at improving the quality of dehazed images by fully utilizing the information in the training dataset. The backbone of LFR-Net is structured by feature residual block and adaptive feature fusion model. Furthermore, to preserve more details for the recovered clear images, we design an adaptive feature fusion model that adaptively fuses shallow and deep features at each scale of the encoder and decoder. Extended experiments show that the performance of our LFR-Net outperforms the state-of-the-art methods.

Quantum computers have provided a promising tool for tackling NP hard problems. However, most of the existing work on quantum annealers assumes exclusive access to all resources available in a quantum annealer. This is not resource efficient if a task consumes only a small part of an annealer and leaves the rest wasted. We ask if we can run multiple tasks in parallel or concurrently on an annealer, just like the multitasking capability of a classical general-purpose processor. By far, multitasking is not natively supported by any of the existing annealers. In this paper, we explore Multitasking in Quantum Annealer (QAMT) by identifying the parallelism in a quantum annealer from the aspect of space and time. Based on commercialised quantum annealers from D-Wave, we propose a realisation scheme for QAMT, which packs multiple tasks into a quantum machine instruction (QMI) and uses predefined sampling time to emulate task preemption. We enumerate a few scheduling algorithms that match well with QAMT and discuss the challenges in QAMT. To demonstrate the potential of QAMT, we simulate a quantum annealing system, implement a demo QAMT scheduling algorithm, and evaluate the algorithm. Experimental results suggest that there is great potential in multitasking in quantum annealing.