Constructive interference (CI) is a synchronous transmission technique for multiple senders transmitting the same packet simultaneously in wireless sensor networks (WSNs). CI enables fast and reliable network flooding in order to reduce the scheduling overhead of MAC protocols, to achieve accurate time synchronization, to improve link quality of lossy links, and to realize efficient data collection. By achieving microsecond level time synchronization, Glossy realizes millisecond level CI-based flooding and 99% reliability. However, Glossy produces substantial unnecessary data forwarding, which significantly reduces the network lifetime. This is a very critical problem, especially in energy-limited large-scale wireless sensor networks for agriculture and forestry (WSN-AF) system. In this paper, we present an energy adaptive CI-based flooding protocol (EACIF) by exploiting CI in WSN-AF. EACIF proposes a distributed active nodes selection algorithm (ANSA) to reduce redundant transmissions, thereby significantly reducing energy consumption and flooding latency. We estimate the performance of EACIF both with real data traces and with uniformly distributed topology. Simulation results show that EACIF achieves almost the same packet reception ratio (PRR) as Glossy (e.g., 99%), while reducing 63.96% energy consumption. EACIF also reduces 25% flooding latency. When the packet interval is 30 seconds, EACIF achieves 0.11% duty cycle.
Brain imaging technology is an important means to study brain diseases. The commonly used brain imaging technologies are fMRI and EEG. Clinical practice has shown that although fMRI is superior to EEG in observing the anatomical details of some diseases that are difficult to diagnose, its costs are prohibitive. In particular, more and more patients who use metal implants cannot use this technology. In contrast, EEG technology is easier to implement. Therefore, to break through the limitations of fMRI technology, we propose a brain imaging modality transfer framework, namely BMT-GAN, based on a generative adversarial network. The framework introduces a new non-adversarial loss to reduce the perception and style difference between input and output images. It also realizes the conversion from EEG modality data to fMRI modality data and provides comprehensive reference information of EEG and fMRI for radiologists. Finally, a qualitative and quantitative comparison with the existing GAN-based brain imaging modality transfer approaches demonstrates the superiority of our framework.
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