Performance of Cross-layer Design with Multiple Outdated Estimates in Multiuser MIMO System

By combining adaptive modulation (AM) and automatic repeat request (ARQ) protocol as well as user scheduling, the cross-layer design scheme of multiuser MIMO system with imperfect feedback is presented, and multiple outdated estimates method is proposed to improve the system performance. Based on this method and imperfect feedback information, the closed-form expressions of spectral efficiency (SE) and packet error rate (PER) of the system subject to the target PER constraint are respectively derived. With these expressions, the system performance can be effectively evaluated. To mitigate the effect of delayed feedback, the variable thresholds (VTs) are also derived by means of the maximum a posteriori method, and these VTs include the conventional fixed thresholds (FTs) as special cases. Simulation results show that the theoretical SE and PER are in good agreement with the corresponding simulation. The proposed CLD scheme with multiple estimates can obtain higher SE than the existing CLD scheme with single estimate, especially for large delay. Moreover, the CLD scheme with VTs outperforms that with conventional FTs

Incomplete Information based Collaborative Computing in Emergency Communication Networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Due to the urgent and unpredictable nature of disaster relief, emergency management systems (EMS) faces an enormous challenge of real-time data analysis without the complete information from emergency communication networks (ECNs). In this letter, we propose an incomplete information based twotier game model (IITG) to realize collaborative computing at the edge of ECNs, which incentivizes idle computing devices (ICDs) to share computation resources through maximizing utilities of EMS and ICDs. Furthermore, we develop a near-optimal IITG algorithm (N-IITG) to seek the unique Bayesian Nash equilibrium. Simulation results reveal that N-IITG outperforms the existing incomplete information based methods in terms of computation latency and participants utilities

Temporal stability of soil moisture spatial variability at two scales and its implication for optimal field monitoring

International audienceSoil moisture spatial distribution is a key component in characterizing and modeling water movement at multiple scales. The temporal stability of soil moisture spatial distribution at multiple depths was investigated at the 7.9-ha Shale Hills Catchment in central Pennsylvania with a year-round monitoring of 77 sites distributed across the catchment. For this catchment with heterogeneous soils and landforms, integration of soils information into the temporal stability assessment provided a more accurate location of representative monitoring sites for capturing mean soil moisture. The temporal stability pattern of soil moisture at the swale scale was similar to that at the catchment scale, suggesting that the swale could be used as a representative unit in the catchment study in terms of mean soil moisture dynamics. The temporal stability of soil moisture variability in this catchment varied over space and seasons. Temporally stable sites were found in the northwestern and southeastern parts of the catchment, while the areas near the stream and some swale areas had lower temporal stability. The spatial distribution of soil moisture was more stable over time during wet seasons, but less stable during transitional periods (i.e. drying or recharging periods). The temporal stability concept helps the optimal design of field monitoring sites and sampling strategies. On the other hand, the temporally unstable sites provide insights regarding the hydrological processes behind the spatial variability of soil moisture

Transmutation prospect of long-lived nuclear waste induced by high-charge electron beam from laser plasma accelerator

Photo-transmutation of long-lived nuclear waste induced by high-charge relativistic electron beam (e-beam) from laser plasma accelerator is demonstrated. Collimated relativistic e-beam with a high charge of approximately 100 nC is produced from high-intensity laser interaction with near-critical-density (NCD) plasma. Such e-beam impinges on a high-Z convertor and then radiates energetic bremsstrahlung photons with flux approaching 10^{11} per laser shot. Taking long-lived radionuclide ^{126}Sn as an example, the resulting transmutation reaction yield is the order of 10^{9} per laser shot, which is two orders of magnitude higher than obtained from previous studies. It is found that at lower densities, tightly focused laser irradiating relatively longer NCD plasmas can effectively enhance the transmutation efficiency. Furthermore, the photo-transmutation is generalized by considering mixed-nuclide waste samples, which suggests that the laser-accelerated high-charge e-beam could be an efficient tool to transmute long-lived nuclear waste.Comment: 13 pages, 8 figures, it has been submitted to Physics of Plasm

Condition assessment of heritage timber buildings in operational environments

© 2017, Springer-Verlag GmbH Germany. Due to changing environments and aging, the structural resistance of the heritage buildings has been reduced significantly. It has become crucial to monitor and protect the architectural heritage buildings. The objective of this research is to monitor and assess the performance of the heritage Tibetan timber building in operational environments. A three-storey corridor part of the typical heritage building was chosen in the study. A long-term monitoring system was installed in the building to collect the structural response and temperature. Detailed finite element model was built based on site investigation and existing documents, and updated based on the temperature-based response sensitivity using the field-monitoring data. The updated model was further evaluated using the static and dynamic analysis for condition assessment of the building in operational environments. The results show that the updated model is effective and accurate to predict the structural behaviour of the building in operational environments. Based on temperature-based response sensitivity, it is capable of tracking structure performance throughout the life-cycle allowing for condition-based maintenance and structural protection

Imbalanced Deep Learning by Minority Class Incremental Rectification

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.Comment: Accepted for IEEE Trans. Pattern Analysis and Machine Intelligenc