In complex indoor environments, non-line-of-sight (NLOS) propagation severely degrades the precision of ultra-wideband (UWB) ranging. Existing NLOS identification methods primarily rely on statistical features or waveform analysis from single-modal channel impulse response (CIR) data, but could be failed when the NLOS CIR is similar as the LOS one. Fortunately, image vision can provide more abundant information about the ranging environment and offer spatial features that can be coordinated with CIR temporal characteristics. Based on the above insight, this paper proposes a multimodal collaborative perception framework (MCPF). Transceiver side images are compressed into embeddings by convolutional neural networks (CNNs), while CIR acquires embedded representations through 1 dimensional convolutional layers combined with LSTM modules. Adaptive weight allocation dynamically fuses these cross-modal features, enabling a lightweight fully-connected classifier to distinguish NLOS conditions. To optimize cross-modal interactions, this paper further designs a tailored composite loss function specifically for the multimodal architecture. Experimental validation on a field-collected dataset spanning eight LOS/NLOS scenarios demonstrates that MCRF reaches 94.31%, and significantly outperforms the classical single modal methods (e.g., kurtosis, LSTM, CNN-LSTM) by 10.59-26.53 percentages.

Software configuration requires that the user sets appropriate values to specified variables, known as configuration parameters, which potentially affect the behaviors of software system. It is an essential means for software reliability, but how to ensure correct configurations remains a great challenge, especially when a large number of parameter settings are involved. Existing studies on misconfiguration diagnosis treat all configurations independently, ignoring the constraints and correlations among different configurations. In this article, we reveal the phenomenon of multi-misconfigurations and present a tool, MMD, for multi-misconfigurations diagnosis. Specifically, MMD consists of two modules: Correlated Configurations Analysis and Primary Misconfigurations Diagnosis. The former determines the correlation among each pair of configurations by analyzing the control and data flows related to each configuration. The latter is responsible for collecting a list of configurations ranked according to their suspiciousness. Combining the outputs of two modules, MMD is able to assist the user in multi-misconfigurations diagnosis. We evaluate MMD on seven popular Java projects: Randoop, Soot, Synoptic, Hdfs, Hbase, Yarn, and Zookeeper. MMD identifies 510 configuration correlations with a 4.9% false positive rate. Furthermore, it effectively diagnoses 22 multi-misconfigurations collected from StackOverflow, outperforming two state-of-the-art baselines.