The Unified Task Assignment for Underwater Data Collection With Multi-AUV System: A Reinforced Self-Organizing Mapping Approach.

This article deals with the task assignment problem for multiple autonomous underwater vehicles to efficiently collect underwater data from sensors. We formulate a unified framework to consistently address the heterogeneous task assignment problem (nonemergency and emergency cases) without strictly distinguishing the mixed cases. First, a unified problem, which bridges the gap between different constraints and optimization objectives of different cases, is constructed. Then, the proposed reinforced self-organizing mapping algorithm is reinforced in three aspects: the regional learning rate, the self-configuring neuron (SCN) strategy, and the workload balance mechanism. Specifically, the proposed regional learning rate comprehensively considers the individual worth of tasks and the topology to generate the regional learning rate of dynamic task regions, which consists of dynamic remaining tasks and the reconstructed topology. Based on this idea, the constructed unified problem can be solved consistently. Furthermore, the proposed SCN strategy optimizes the neuron population both in quality and quantity, and guides the update of neurons with enriched historical information to improve the mapping ability. This strategy greatly improves learning efficiency and applicability in a wide range of scenarios. Meanwhile, the proposed workload balance mechanism takes into consideration of both the work capability and consumed energy to extend the continuous working capability. The numerical results validate the effectiveness and adaptability of the proposed unified task assignment framework.

Fourier-based interpretation and noise analysis of the moments of small-angle x-ray scattering in grating-based x-ray phase contrast imaging.

In grating-based x-ray phase contrast imaging, Fourier component analysis (FCA) is usually recognized as a gold standard to retrieve the contrasts including attenuation, phase and dark-field, since it is well-established on wave optics and is of high computational efficiency. Meanwhile, an alternative approach basing on the particle scattering theory is being developed and can provide similar contrasts with FCA by calculating multi-order moments of deconvolved small-angle x-ray scattering, so called as multi-order moment analysis (MMA). Although originated from quite different physics theories, the high consistency between the contrasts retrieved by FCA and MMA implies us that there may be some intrinsic connections between them, which has not been fully revealed to the best of our knowledge. In this work, we present a Fourier-based interpretation of MMA and conclude that the contrasts retrieved by MMA are actually the weighted compositions of Fourier coefficients, which means MMA delivers similar physical information as FCA. Based on the recognized cosine model, we also provide a truncated analytic MMA method, and its computational efficiency can be hundreds of times faster than the original deconvolution-based MMA method. Moreover, a noise analysis for our proposed truncated method is also conducted to further evaluate its performances. The results of numerical simulation and physical experiments support our analyses and conclusions.

Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks.

Cooperative transmission is a promising technology for underwater acoustic sensor networks (UASNs) to ensure the effective collection of underwater information. In this paper, we study the joint relay selection and power allocation problem to maximize the cumulative quality of information transmission in energy harvesting-powered UASNs (EH-UASNs). First, we formulate the process of cooperative transmission with joint strategy optimization as a Markov decision process model. In the proposed model, an effective state expression is presented to better reveal interactive relationship between learning and environment, thereby improving the learning ability. Then, we further propose a novel reward function which can guide nodes to adjust power strategy adaptively to balance instantaneous capacity and long-term quality of service (QoS) under the dynamic unpredictable energy harvesting. More specifically, we propose a deep Q-network-based resource allocation algorithm for EH-UASNs to solve the complex coupled strategy optimization problem without any prior underwater environment information. Finally, simulation results verify the superior performance of the proposed algorithm in improving the cumulative network capacity and reducing outages.

Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning Formulation.

The autonomous underwater glider has attracted enormous interest for underwater activities, especially in long-term and large-scale underwater data collection. In this paper, we focus on the application of gliders gathering data from underwater sensor networks over underwater acoustic channels. However, this application suffers from a rapidly time-varying environment and limited energy. To optimize the performance of data collection and maximize the network lifetime, we propose a distributed, energy-efficient sensor scheduling algorithm based on the multi-armed bandit formulation. Besides, we design an indexable threshold policy to tradeoff between the data quality and the collection delay. Moreover, to reduce the computational complexity, we divide the proposed algorithm into off-line computation and on-line scheduling parts. Simulation results indicate that the proposed policy significantly improves the performance of the data collection and reduces the energy consumption. They prove the effectiveness of the threshold, which could reduce the collection delay by at least 10% while guaranteeing the data quality.

The trigonometric orthogonality of phase-stepping curves in grating-based x-ray phase-contrast imaging: Integral property and its implications for noise optimization.

PURPOSE: Grating-based x-ray phase-contrast imaging (GPCI) is a promising technique for clinical applications as it can provide two newly emerging imaging modalities (differential phase-contrast and dark-field contrast) in addition to the conventional absorption contrast. As far, phase-stepping strategy is the most commonly used approach in GPCI to indirectly acquire differential phase-contrast and dark-field contrast. It is known that the obtained phase-stepping curves (PSCs) have the cosine property and the convolution property, leading to two types of information retrieval approaches in literature: the Fourier component analysis and the multi-order moment analysis. The purpose of this paper is to derive a new property of PSCs and apply the property to noise optimization for information retrieval. METHODS: Based on the cosine expression of the flat PSC without the sample and the well-established convolution relationship between the flat PSC and the sample PSC, we reveal an important integral property of PSCs: the inner product of PSCs and an arbitrary function contains only zero-order and first-order components in the Fourier series. Furthermore, we apply the property to the direct multi-order moment analysis and propose a set of generalized forms including an optimal one in the presence of noise. RESULTS: To validate the effectiveness of our analysis, we compared the simulated and real experiment results retrieved by the original direct multi-order moment analysis with the ones retrieved by our proposed noise-optimal form. A significant improvement of noise performance by our method is observed and the improvement ratio in differential phase-contrast is consistent with our theoretical calculation (39.2%). CONCLUSIONS: In this paper, we reveal a new integral property of the acquired PSCs with and without samples in GPCI, which can be applied to information retrieval approaches like the direct multi-order moment analysis. Then we optimize these approaches to improve the noise performance, offering great potentials of dose reduction in practical applications.

Quantitative characterization of ex vivo breast tissue via x-ray phase-contrast tomography.

BACKGROUND: Grating-based X-ray phase-contrast imaging (GPCI) has received growing interests in recent years due to its high capability of visualizing soft tissue. Breast imaging is one of the most promising candidates for the first clinical application of this imaging modality. OBJECTIVE: In this work, quantitative breast tissue characterization based on GPCI computed tomography (CT) is investigated with a laboratory X-ray tube through a comparison between attenuation-based CT images and phase-contrast CT images. METHODS: The Hounsfield units (HU) scale was introduced to phase-contrast images due to its wide application in clinical medicine. In this work, instead of water, plastic cylinders composed of polyethylene terephthalate (PET) was treated as the calibration material. An alternative test-retest reliability (TRR) was presented to evaluate the repeatability of GPCI. Comparison between attenuation-based CT imaging and GPCI CT imaging was operated with the use of statistical analysis methods like histograms and receiver operating characteristic (ROC) curves. RESULTS: The determined mean TRR related to cylinders is slightly larger in phase-contrast imaging (0.93) than that in attenuation-based imaging (0.89). With respect to distinguishing breast tissues, the AUC (area under curve) values of ROC curves of phase-contrast images are higher than that of attenuation-based images. CONCLUSIONS: An ex vivo study of GPCI shows that it is a stable imaging modality for visualizing the breast tissue with good repeatability, and that it could be of potential for the diagnosis of breast cancer as well.

A comparative study of information retrieval in grating-based x-ray phase-contrast imaging.

The cosine-model analysis (CMA) method and the small angle x-ray scattering (SAXS) method are two major types of information retrieval algorithms, commonly utilized in x-ray phase-contrast imaging with a grating interferometer. However, there are significant differences between the two methods in algorithm implementation, and the existing literature has not completely revealed their intrinsic relationship. In this paper, we theoretically derive and experimentally verify the intrinsic connections between CMA and SAXS, and it is seen that SAXS can be interpreted well by the cosine-model assumption of CMA. To validate our analysis of the scattering distribution when applying the cosine model to the convolution used in SAXS, we applied a deconvolution process into CMA before using the Fourier transform to get the three contrasts. Furthermore, the principal component analysis (PCA) is introduced in this work, and two PCA-based retrieval algorithms are presented in order to simplify the iteration process of deconvolution in SAXS or to obtain absorption and dark-field signals instead of the Fourier transform in CMA. Applying a quantitative structural similarity (SSIM) index and a profile analysis to the results of an ex vivo mammography, it is proved that retrieved images via CMA and SAXS are consistent with each other (SSIM values are 1.0000, 0.9845 and 0.9767 respectively), and that the extra deconvolution process applied into CMA shows a good performance and our analytical analysis of the scattering distribution is valid when applying the cosine model to the convolution used in SAXS. Besides, it is concluded that PCA shows almost the same performance with the Fourier transform (SSIM values are 1.0000 for both absorption and dark-field images), and the simplified SAXS-analogous method works well with higher efficiency in computation and better stability relative to the original SAXS, while maintaining the similar level of image quality (SSIM values are 1.0000, 0.9839 and 0.9781 respectively).

Diagnosis of breast cancer based on microcalcifications using grating-based phase contrast CT.

OBJECTIVES: Microcalcifications are an important feature in the diagnosis of breast cancer, especially in the early stages. In this paper, a CT-based method is proposed to potentially distinguish benign and malignant breast diseases based on the distributions of microcalcifications using grating-based phase-contrast imaging on a conventional X-ray tube. METHODS: The method presented based on the ratio of dark-field signals to attenuation signals in CT images is compared with the existing method based on the ratio in projections, and the threshold for the classification of microcalcifications in the two types of breast diseases is obtained using our approach. The experiment was operated on paraffin-fixed specimens that originated from 20 female patients ranging from 27-65 years old. RESULTS: Compared with the method based on projection images (AUC = 0.87), the proposed method is more effective (AUC = 0.95) to distinguish the two types of diseases. The discrimination threshold of microcalcifications for the classification of diseases in CT images is found to be 3.78 based on the Youden index. CONCLUSIONS: The proposed method can be further developed to improve the early diagnosis and diagnostic accuracy and reduce the clinical misdiagnosis rate of breast cancer. KEY POINTS: • Microcalcifications are of special importance to indicate early breast cancer. • Grating-based phase-contrast imaging can improve the diagnosis of breast cancers. • The method described here can better classify benign and malignant breast diseases.

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.