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This paper presents an idea to further simplify and relax the linear matrix inequality (LMI) stability conditions of Takagi-Sugeno (T-S) fuzzy systems. By considering the distribution of membership functions in a unified space, we can easily find the overall relation of the original nonlinear system and its approximated local subsystems. Based on the theory of convex combination, the upper-bounds and lower-bounds of each membership functions can be directly used to construct less conservative LMI conditions. The cases of both Type-1 and Type-2 systems are considered, and examples are provided to illustrate the achieved improvement.

In this paper, acoustic wave propagation in water-saturated sand under controlled laboratory conditions is investigated. Our method uses a combination of two fundamental frequencies f 1 & f 2 generated using piezoelectric transducers placed on the surface of the sand, simulating seabed with fresh water and sand. The interaction of sound (acoustic) waves in a dispersive but homogenous medium will typically generate harmonics and intermodulation terms (intermod). These interactions result in perturbations as the sum and difference frequencies. The results show that the amplitude of the intermodulation products is inversely proportional to the density and thickness of the buried inhomogeneity. For instance, 2nd order intermod term show 2 dB decreases in amplitude for 7 mm increase in thickness at constant density of plywood. For constant thickness at 5 mm there is 10 dB drop in same intermod term for change in density from plywood 550 Kg/m3 to copper 8930 Kg/m3.

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.

The measurement of an emitter's position using electronic support passive sensors is termed passive geolocation. The emitting target could be in underwater environment. In this paper, we propose a Time Difference of Arrival (TDoA) algorithm for passive geolocation in underwater environment. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. To estimate the delay for TDoA of two correlated channels, we propose an TDoA finding algorithm, and compare with existing approach. Simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and Root-Mean-Square-Error (RMSE). In underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.