The attenuation (sum of absorption and scattering), which is caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. In this study, we describe an underwater image enhancement method based on adaptive attenuation-curve prior. This method uses color channel transfer (CCT) to preprocess the underwater images, light smoothing, and wavelength-dependent attenuation to estimate water light and obtain the attenuation ratio between color channels, and estimates and refines the initial relative transmission of the channel. Additionally, the method calculates the attenuation factor and saturation constraints of the three color channels and generates an adjusted reverse saturation map (ARSM) to address uneven light intensity, after which the image is restored through water light and transmission estimation. Furthermore, we applied white balance fusion globally guided image filtering (G-GIF) technology to achieve color enhancement and edge detail preservation in the underwater images. Comparison experiments showed that the proposed method obtained better color and de-hazing effects, as well as clearer edge details, relative to current methods.

The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results.

Medical emergency monitoring body sensor networks (BSNs) monitor the occurrence of medical emergencies and are helpful for the daily care of the elderly and chronically ill people. Such BSNs are characterized by rare traffic when there is no emergency occurring, high real-time and reliable requirements of emergency data and demand for a fast wake-up mechanism for waking up all nodes when an emergency happens. A beacon-enabled MAC protocol is specially designed to meet the demands of medical emergency monitoring BSNs. The rarity of traffic is exploited to improve energy efficiency. By adopting a long superframe structure to avoid unnecessary beacons and allocating most of the superframe to be inactive periods, the duty cycle is reduced to an extremely low level to save energy. Short active time slots are interposed into the superframe and shared by all of the nodes to deliver the emergency data in a low-delay and reliable way to meet the real-time and reliable requirements. The interposition slots can also be used by the coordinator to broadcast network demands to wake-up all nodes in a low-delay and energy-efficient way. Experiments display that the proposed MAC protocol works well in BSNs with low emergency data traffic.

For semantic segmentation of cardiac magnetic resonance image (MRI) with low recognition and high background noise, a fusion‐attention Swin Transformer is proposed based on cognitive science and deep learning methods. It has a U‐shaped symmetric encoding–decoding structure with an attention‐based skip connection. The encoder realizes self‐attention for deep feature representation and the decoder up‐samples global features to the corresponding input resolution for pixel‐level segmentation. By introducing a skip connection between the encoder and decoder based on fusion attention, the remote interaction of global information is realized, and the attention to local features and specific channels is enhanced. A public ACDC cardiac MRI image dataset is used for experiments. The segmentation of the left ventricle, right ventricle, and myocardial layer is realized. The method performs well on a small sample dataset, for example, the pixel accuracy obtained by the proposed model is 93.68%, the Dice coefficient is 92.28%, and HD coefficient is 11.18. Compared with the state‐of‐the‐art models, the segmentation precision has been significantly improved, especially for the low recognition and heavily occluded targets.