A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays inversely with respect to the square of distance from the source. By comparing energy readings measured at surrounding acoustic sensors, the source location during that time interval can be accurately estimated as the intersection of multiple hyperspheres. Theoretical bounds on the number of sensors required to yield unique solution are derived. Extensive simulations have been conducted to characterize the performance of this method under various parameter perturbations and noise conditions. Potential advantages of this approach include low intersensor communication requirement, robustness with respect to parameter perturbations and measurement noise, and low-complexity implementation.

Channel state information (CSI) based fingerprinting for WIFI indoor localization has attracted lots of attention very recently. The frequency diverse and temporally stable CSI better represents the location dependent channel characteristics than the coarse received signal strength (RSS). However, the acquisition of CSI requires the cooperation of access points (APs) and involves only data frames, which imposes restrictions on real-world deployment. In this paper, we present CRISLoc, the first CSI fingerprinting based localization prototype system using ubiquitous smartphones. CRISLoc operates in a completely passive mode, overhearing the packets on-the-fly for his own CSI acquisition. The smartphone CSI is sanitized via calibrating the distortion enforced by WiFi amplifier circuits. CRISLoc tackles the challenge of altered APs with a joint clustering and outlier detection method to find them. A novel transfer learning approach is proposed to reconstruct the high-dimensional CSI fingerprint database on the basis of the outdated fingerprints and a few fresh measurements, and an enhanced KNN approach is proposed to pinpoint the location of a smartphone. Our study reveals important properties about the stability and sensitivity of smartphone CSI that has not been reported previously. Experimental results show that CRISLoc can achieve a mean error of around 0.29m in a 6m×8m research laboratory. The mean error increases by 5.4 cm and 8.6 cm upon the movement of one and two APs, which validates the robustness of CRISLoc against environment changes.

A new tensor-based approach, which is capable of significantly increasing the degrees of freedom (DOF) of an L-shaped array consisting of two orthogonal uniform linear arrays (ULAs), is proposed. By dividing each ULA into some overlapping subarrays and then combining their received signals into a data tensor, a new cross-correlation tensor between the two data tensors from the two ULAs is built. Analyses show that such a cross-correlation tensor can be transformed into an equivalent received data matrix of a virtual rectangular array (URA). Under the constraint of a fixed actual number of physical sensors, the optimal number of the subarrays is found by maximizing the DOF of the URA. It is shown that a virtual URA with approximately $0.34{{(M+1)}^{2}}$ DOF can be obtained from an L-shaped array with $2M$ physical sensors. To exploit the increased DOF for the 2-D DOA estimation without the multidimensional search, a parallel factor (PARAFAC) model of the URA is provided so that the PARAFAC decomposition can be used to do the 2-D DOA estimation effectively. Simulation results show that the proposed method can yield a better estimation performance and resolve more sources than the computationally efficient methods reported in literature.

Online and offline monitoring of composite bolted joints under tensile load were investigated using piezoelectric transducers. The relationships between Lamb wave signals, pre-tightening force, the applied tensile load, as well as the failure modes were investigated. Results indicated that S0/A0 wave amplitudes decrease with the increasing of load. Relationships between damage features and S0/A0 mode were built based on the finite element (FE) simulation and experimental results. The possibility of application of Lamb wave-based structure health monitoring in bolted joint-like composite structures was thus achieved.

In human vision, it has been well understood that the RGB (red, green, and blue) trichromatic system, whose colors can be well expressed by an RGB hue ring with complementary color relations, is the most accessible and extensible color representation. However, such color relations rarely play a role in color image/video processing tools such as wavelets. In this paper, the gap between wavelets and complementary color relations is filled by designing a novel color image/video processing tool called the Complementary Color Wavelet Transform (CCWT). Our novel wavelets consist of a family of well-designed 2D complex wavelets with 2π/3 phase differences copied from the R, G, and B angle relations on the RGB hue ring. Analyses show that our new wavelets can not only utilize the complementary color relations but also inherit and improve upon the advantages of classical complex wavelets such as their multi-resolution, directional selectivity, and shift-invariance properties. By the newly designed wavelets and by properly adding and/or subtracting the wavelet coefficients of a color image/video, some color-related image/video processing applications that are typically seen as infeasible or difficult to achieve by traditional methods, for instance, capturing and classifying local color changes, statistical modeling of the wavelet coefficients among the color channels, and multi-resolution directional color filtering, can be performed very easily. The cost for this is simply a transform redundancy increase (2.25 times that of classical complex wavelets). We provide three application examples of our method: color filtering, texture retrieval, and video quality assessment (VQA). The results show that our new wavelet tool greatly outperforms traditional methods.