The limited bandwidth of Wi-Fi severely confines the granularity (especially in differentiating multiple subjects) of Wi-Fi sensing, posing a significant challenge for its wide adoption. Though utilizing multiple channels to expand the effective bandwidth sounds plausible, continuous spectrum stitching towards ultra-wideband (UWB) is far from practical given various constraints (e.g., the runtime channel availability and inconsistent channel responses across a wide bandwidth). To this end, we propose UWB-Fi as a novel Wi-Fi sensing system with ultra-wide bandwidth, leveraging only discrete and irregular channel sampling. We first design a fast channel hopping scheme to perform arbitrary sampling across 4.7GHz (i.e., 2.4 to 7.1GHz) bandwidth on commodity Wi-Fi hardware without interrupting default communications. As no signal processing tool is available to handle such channel samples, we innovate in a model-based deep learning approach that translates discrete channel samples to high-dimensional spectral parameters; this method successfully avoids the bias-variance tradeoff in parameter estimation, while filtering out hardware-related offsets inherent to Wi-Fi. Through extensive evaluations, we demonstrate that UWB-Fi successfully achieves fine-granularity sensing, enabling centimeter-level resolution for indoor multi-person sensing.

An echo state network with improved topology (IESN) is proposed for accurate and efficient time series prediction. In this approach, a tighter bound of the echo state property related to the Lipshitz constant of reservoir activation function and the maximum structured singular value of reservoir is firstly researched to run the model at the edge of chaos. A smooth composite reservoir activation function is then designed to enhance the ESN. The exact echo state property bound is solved by computing the Lipshitz constant of the composite function. Finally, a decoupling matrix with eigenvalues distributing uniformly in the complex plane is built as the reservoir for abundant dynamic characteristics. Six classical benchmarks are employed to test the IESN. Besides, combined with amplitude-frequency separation based on the Hilbert transform, the IESN predicts a set of engine vibration signals in knock. Compared with several popular models, the proposed IESN shows the best performance.

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown objects while integrating learned knowledge for future tasks. However, the level of "unknownness" varies significantly depending on the context. For example, a tree is typically considered part of the background in a self-driving scene, but it may be significant in a household context. We argue that this contextual information should already be embedded within the known classes. In other words, there should be a semantic or latent structure relationship between the known and unknown items to be discovered. Motivated by this observation, we propose Hyp-OW, a method that learns and models hierarchical representation of known items through a SuperClass Regularizer. Leveraging this representation allows us to effectively detect unknown objects using a similarity distance-based relabeling module. Extensive experiments on benchmark datasets demonstrate the effectiveness of Hyp-OW, achieving improvement in both known and unknown detection (up to 6 percent). These findings are particularly pronounced in our newly designed benchmark, where a strong hierarchical structure exists between known and unknown objects.

Engine fault detection is critical to enhancing the reliability of modern equipment. However, it is challenging to obtain a large number of high-quality labeled data for engines, which is not conducive to improving the training accuracy of deep learning methods. Therefore, this article proposes a fault detection method combining adaptive recursive variational mode decomposition (ARVMD) and component energy distribution spectrum (CEDS). The article first introduces recursive mode into VMD. Then, the mode number is dynamically selected according to the energy distribution of the power spectral density (PSD) to extract the intrinsic mode functions (IMFs) continuously. The quadratic penalty term is optimized correspondingly using SNR. The decomposition results of artificial and real signals demonstrated that ARVMD has higher SNR and efficiency than VMD. Next, the center frequency and unit bandwidth energy of IMF are used to construct CEDS. Fault diagnosis is realized by the CEDS correlation ranking of various faults. Finally, two case studies are performed to illustrate the effectiveness of the proposed method. The results show that the proposed ARVMD-CEDS method provides an efficient and effective solution for single-channel engine fault diagnosis.