With the widespread adoption of large-scale mobile smart devices, Mobile Crowdsensing is evolving from a single-platform paradigm toward a multi-platform architecture. In such multi-platform environments, ensuring task completion while maintaining a balanced user borrowing-to-lending (BL) ratio across platforms has emerged as a critical challenge, particularly in enhancing the quality of sensing data. To address this issue, we propose a Multi-stage High-quality Data-driven Incentive Mechanism (MHDIM). The mechanism comprises three sequential stages. In the first stage, we design the ITA algorithm, which jointly considers user data quality and task urgency to allocate tasks among intra-platform users. Each platform then uploads information about its idle users and unassigned tasks to a cross-platform management entity. In the second stage, we propose the CMSIP algorithm, which leverages user quality and the BL ratio of each platform to assist the cross-platform entity in assigning tasks with shared Points of Interest. In the third stage, the CMDIP algorithm is introduced to handle cross-platform task allocation involving different points of interest, taking into account user data quality, the spatial distance between users and tasks, and the platform-level BL ratio. Experimental results demonstrate that, compared with existing mainstream incentive mechanisms, the proposed MHDIM significantly outperforms in terms of system utility, task completion rate, and data quality within multi-platform settings.

To improve test data compression efficiency, the order of the scan unit need be adjusted, which indirectly changes the content of the test pattern. Scan chain partitioning is a common method that utilises this concept. However, current scan chain partitioning methods can still be optimised in terms of entropy and test data compression efficiency, and lack universality. To enhance the efficiency of code-based compression efficiency, we propose a scan slice reordering algorithm that minimizes entropy. This algorithm first calculates the rank of each scan slice or column vector of the test set, and then dynamically adjusts the order of the scan slices according to the descending order of these ranks in the pursuit of minimizing the entropy of the test set. By iterating through this process, the optimal position of each scan slice in the test set is ultimately determined. Compression experiments should be performed on all test patterns using different code-based schemes. This not only reduces the entropy of the test set but also significantly improves the efficiency of different codes. Compared to traditional scan chain partitioning method, FDR encoding achieved an average compression ratio increase of 6.16%, and RL-Huffman encoding achieved an average compression ratio increase of 4.96%. The experimental results demonstrate that our proposed algorithm is feasible, effective, and universal.

Fixed-wing unmanned aerial vehicle (UAV) communication is a promising paradigm for providing mobile video services to ground users (GUs) without the need of infrastructure. However, the performance of fixed-wing UAV video streaming is severely limited by the onboard energy while energy-efficient fixed-wing UAV video streaming has not been fully explored. In this paper, we study energy-efficient video streaming with a fixed-wing UAV for provisioning mobile video streaming services to multiple GUs. We define UAV's energy efficiency as the ratio of perceived video quality at all GUs to the UAV's energy consumption and formulate an energy efficiency maximization problem that jointly optimizes the communication time allocation among GUs and the UAV's trajectory. Due to the non-convex nature of the formulated problem, we propose a near-optimal iterative algorithm, which utilizes successive convex approximation and quadratic transform techniques to address the problem efficiently. Extensive simulations demonstrate the high efficiency and effectiveness of our proposed algorithm.

Fractures with fluid flow can lead to the damage of rock carving relics. During the detection of fractures, millimeter-scale fractures are usually difficult to determine due to their small apertures. Considering the rapid variation of water content in the fracture seepage zone can lead to anisotropy, this article proposes a new methodology to detect these millimeter-scale fractures with fluid flow using a time-lapse full-polarimetric ground penetrating radar (FP-GPR) scheme and an anisotropy analysis method. The time-lapse FP-GPR detection can monitor the water flow in the fracture and the infiltration in the rock, and the Freeman decomposition, H-Alpha decomposition, and a polarimetric phase (PP) feature are adopted to quantify and analyze the anisotropic effects over time. In the numerical test, we adopt hydrological modeling to build realistic dielectric models for time-lapse FP-GPR simulations. The results indicate that the variations of water contents and several polarimetric features, i.e., the surface-like scattering power, the double-bounce scattering power, and the averaged scattering angle, are consistent and are essentially related to the anisotropy of the seepage zone. Finally, we introduce the field tests performed at the experimental station of the Dazu Rock Carvings in Chongqing, China, which contain two cases I and II. Case I is an experiment on a surface fracture of a cliff, whereas case II is a detection test of a buried fracture. The results verify the effectiveness of the proposed methodology.

Cavity detection is vital to homeland security, civil engineering, and geological surveys. However, the limited target classification ability of traditional ground penetrating radar (GPR) may lead to its inability to identify cavities in complex environments with multiple anomalies, and the resolution of traditional GPR may not be enough to distinguish the internal structure of cavities. To address this issue, we have developed the dominant scattering mechanism analysis (DSMA) technique of full polarimetric GPR (FP-GPR). This technique calculates the scattering power of three physical models and determines the dominant scattering mechanism of subsurface targets. Cavity identification coefficients (CIC) are then utilized to enhance cavity detection and evaluation accuracy. Numerical simulation experiments have validated the workflow of this cutting-edge method. Scaled model experiments have depicted the polarization characteristics of the scattering field of typical tunnel structures within a laboratory setting. Field experiments have demonstrated that the novel technique can accurately identify and assess tunnels in complex environments with multiple anomalies, providing a foundation for determining regional function.

Incentive mechanisms play a crucial role in mobile crowdsensing between mobile users, third-party platforms, and clients. However, existing mechanisms often do not sufficiently consider users’ future potential and past contributions to data sharing when recruiting or retaining them. Moreover, few people consider that the client in this mechanism requires high-quality data collection for training artificial intelligence models. To solve these problems, this paper proposes an innovative mobile crowdsensing incentive mechanism based on reverse auctions. This mechanism not only attracts users who provide high-quality data but also helps the client collect an appropriate amount of cost-effective data for model training. This is achieved by setting prerequisites for the release of tasks by the platform, that is, only after the client’s model training effect reaches a certain level of improvement, the platform can release new tasks for data collection. Additionally, when recruiting users to participate in auctions, the platform will selectively recruit based on their potential value to ensure high-quality data collection. In cases where users might consider leaving the platform, measures for retention will be implemented. This will involve offering rewards adjusted proportionally according to their potential and contribution value to encourage them to stay. The comprehensive simulation results demonstrate the effectiveness of this new mechanism, highlighting its superior performance over existing mechanisms.

The polarimetric response of full-polarimetric ground penetrating radar (FP-GPR) from anisotropic media is a pressing issue to be investigated. It can improve the detection accuracy of the targets in the anisotropic media and can also provide effective ways to detect anisotropic targets. In this paper, we focus on the FP-GPR signals from uniaxial bi-anisotropic media, i.e. the complete case that both the permittivity and the conductivity of the background media are anisotropic. Based on the propagation characteristics of electromagnetic waves in the anisotropic media and the transmission coefficients on the surface of the media, we construct a mathematical relation between the measured scattering matrix affected by anisotropies and the real scattering matrix, derive a coefficient to characterize the polarization rotation(PR) effects, and proposed a diagonal matrix for target decomposition analyses. The computation results indicate that εz/εx, σz/σx, and antenna interval have complex effects on the PR coefficient and the H-Alpha decomposition results of three types of typical targets. Multiple 3D FP-GPR simulations on a series of uniaxial bi-anisotropic models verified the results.

Detecting abrupt changes in the community structure of a network from noisy observations is a fundamental problem in statistics and machine learning. This paper presents an online change detection algorithm called Spectral-CUSUM to detect unknown network structure changes through a generalized likelihood ratio statistic. We characterize the average run length (ARL) and the expected detection delay (EDD) of the Spectral-CUSUM procedure and prove its asymptotic optimality. Finally, we demonstrate the good performance of the Spectral-CUSUM procedure and compare it with several baseline methods using simulations and real data examples on seismic event detection using sensor network data.

Learning a robust classifier from a few samples remains a key challenge in machine learning. A major thrust of research has been focused on developing k-nearest neighbor (k-NN) based algorithms combined with metric learning that captures similarities between samples. When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier. In this paper, we study a minimax distributionally robust formulation of weighted k-nearest neighbors, which aims to find the optimal weighted k-NN classifiers that hedge against feature uncertainties. We develop an algorithm, Dr.k-NN, that efficiently solves this functional optimization problem and features in assigning minimax optimal weights to training samples when performing classification. These weights are class-dependent, and are determined by the similarities of sample features under the least favorable scenarios. When the size of the uncertainty set is properly tuned, the robust classifier has a smaller Lipschitz norm than the vanilla k-NN, and thus improves the generalization capability. We also couple our framework with neural-network-based feature embedding. We demonstrate the competitive performance of our algorithm compared to the state-of-the-art in the few-training-sample setting with various real-data experiments.

We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. We demonstrate our approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data.

Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world applications. However, rarely can we quantify how well the generative model captures the nature or ground-truth since it is usually unknown. The challenge typically lies in the fact that the generative models typically provide, at most, good approximations to the ground-truth (e.g., through the rich representative power of neural networks), but they cannot be precisely the ground-truth. We thus cannot use the classic goodness-of-fit (GOF) test framework to evaluate their performance. In this paper, we develop a GOF test for generative models of self-exciting processes by making a new connection to this problem with the classical statistical theory of Quasi-maximum-likelihood estimator (QMLE). We present a non-parametric self-normalizing statistic for the GOF test: the Generalized Score (GS) statistics, and explicitly capture the model misspecification when establishing the asymptotic distribution of the GS statistic. Numerical simulation and real-data experiments validate our theory and demonstrate the proposed GS test’s good performance.

We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures. We borrow the idea from the attention mechanism and incorporate it into the point processes’ conditional intensity function. We further introduce a novel score function using Fourier kernel embedding, whose spectrum is represented using neural networks, which drastically differs from the traditional dot-product kernel and can capture a more complex similarity structure. We establish our approach’s theoretical properties and demonstrate our approach’s competitive performance compared to the state-of-the-art for synthetic and real data.

We present a novel adversarial detector for the anomalous sequence when there are only one-class training samples. The detector is developed by finding the best detector that can discriminate against the worst-case, which statistically mimics the training sequences. We explicitly capture the dependence in sequential events using the marked point process with a deep Fourier kernel. The detector evaluates a test sequence and compares it with an optimal time-varying threshold, which is also learned from data. Using numerical experiments on simulations and real-world datasets, we demonstrate the superior performance of our proposed method.

We present an online community change detection algorithm called spectral CUSUM to detect the emergence of a community using a subspace projection procedure based on a Gaussian model setting. Theoretical analysis is provided to characterize the average run length (ARL) and expected detection delay (EDD), as well as the asymptotic optimality. Simulation and real data examples demonstrate the good performance of the proposed method.

Timing resilient design has shown significant promise in mitigating the excess margins associated with rare worst-case data and increased process, voltage, and temperature variations. However, resilient circuits need error detecting sequential logic (EDL) to detect timing errors which incur area and power overhead. This paper proposes two alternatives to reduce the overhead in two-phase latch-based resilient circuits. The first is a new resiliency-aware graph-based approach to solve the retiming problem. The second uses a virtual resynthesis library to enable commercial synthesis tools to recognize the EDL overhead and optimize total area during retiming. We compare both approaches to a commercially standard retiming approach, which ignores the resiliency overheads, on a wide variety of benchmarks. Our experimental results show that our methods are computationally efficient and reduce the total circuit area by an average of up to 10%-15% when compared to traditional retiming.

Timing resilient design has shown significant promise in mitigating the excess margins associated with rare worst-case data and increased process, voltage, and temperature (PVT) variations. However, resilient circuits need error detecting sequential logic (EDL) to detect timing errors which represents area and power overhead. This article proposes a new network-simplex-based retiming method for two-phase latch-based resilient circuits to reduce the overhead of the combination of normal and error detecting latches. Our experimental results show that our method is computationally efficient and reduces the cost of the sequential elements by an average of up to 20% when compared to traditional methods.

Polarization is a property of electromagnetic wave that generally refers to the orientation of the electric field vector, which can be used to characterize target properties by polarimetric radar. Polarimetric decomposition methods have been common in the terrain and land-use classification based on plolarimetric synthetic aperture radar data. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we introduce the polarimetric decomposition technology into the full polarimetric GPR data analysis. Using the polarimetric decomposition, we extract the polarimetric information of subsurface targets. Also migration can be used to the full polarimetric GPR data, and target imaging can be obtained. Laboratory experiment results show that we can improve the classification of subsurface targets by combing the polarimetric information and imaging.