A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs

Coordinated multi-point (CoMP) schemes have been widely studied in the recent years to tackle the inter-cell interference. In practice, latency and throughput constraints on the backhaul allow the organization of only small clusters of base stations (BSs) where joint processing (JP) can be implemented. In this work we focus on downlink CoMP-JP with multiple antenna user equipments (UEs) and propose a novel dynamic clustering algorithm. The additional degrees of freedom at the UE can be used to suppress the residual interference by using an interference rejection combiner (IRC) and allow a multistream transmission. In our proposal we first define a set of candidate clusters depending on long-term channel conditions. Then, in each time block, we develop a resource allocation scheme by jointly optimizing transmitter and receiver where: a) within each candidate cluster a weighted sum rate is estimated and then b) a set of clusters is scheduled in order to maximize the system weighted sum rate. Numerical results show that much higher rates are achieved when UEs are equipped with multiple antennas. Moreover, as this performance improvement is mainly due to the IRC, the gain achieved by the proposed approach with respect to the non-cooperative scheme decreases by increasing the number of UE antennas.Comment: 27 pages, 8 figure

Application of remote sensing to state and regional problems

The methods and procedures used, accomplishments, current status, and future plans are discussed for each of the following applications of LANDSAT in Mississippi: (1) land use planning in Lowndes County; (2) strip mine inventory and reclamation; (3) white-tailed deer habitat evaluation; (4) remote sensing data analysis support systems; (5) discrimination of unique forest habitats in potential lignite areas; (6) changes in gravel operations; and (7) determining freshwater wetlands for inventory and monitoring. The documentation of all existing software and the integration of the image analysis and data base software into a single package are now considered very high priority items

Nondestructive evaluation and in-situ monitoring for metal additive manufacturing

Powder-based additive manufacturing (AM) technologies are seeing increased use, particularly because they give greatly enhanced design flexibility and can be used to form components that cannot be formed using subtractive manufacturing. There are fundamental differences in the morphology of additively manufactured materials, when compared with, for example castings or forgings. In all cases it is necessary to ensure that parts meet required quality standards and that “allowable” anomalies can be detected and characterized. It is necessary to understanding the various types of manufacturing defects and their potential effects on the quality and performance of AM, and this is a topic of much study. In addition, it is necessary to investigate quality from powder throughout the manufacturing process from powder to the finished part. In doing so it is essential to have metrology tools for mechanical property evaluation and for appropriate anomaly detection, quality control, and monitoring. Knowledge of how and when the various types of defects appear will increase the potential for early detection of significant flaws in additively manufactured parts and offers the potential opportunity for in-process intervention and to hence decrease the time and cost of repair or rework. Because the AM process involves incremental deposition of material, it gives unique opportunities to investigate the material quality as it is deposited. Due to the AM processes sensitivity to different factors such as laser power and material properties, any changes in aspects of the process can potentially have an impact on the part quality. As a result, in-process monitoring of additive manufacturing (AM) is crucial to assure the quality, integrity, and safety of AM parts. To meet this need there are a variety of sensing methods and signals which can be measured. Among the available measurement modalities, acoustic-based methods have the advantage of potentially providing real-time, continuous in-service monitoring of manufacturing processes at relatively low cost. In this research, the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capabilities of existing characterization methodologies for powder-based AM parts are discussed and methods for in-situ non-destructive evaluation are reviewed. A proof-of-concept demonstration for acoustic measurements used for monitoring both machine and material state is demonstrated. The analyses have been performed on temporal and spectral features extracted from the acoustic signals. These features are commonly related to defect formation, and acoustic noise that is generated and can potentially characterize the process. A novel application of signal processing tools is used for identification of temporal and spectral features in the acoustic signals. A new approach for a K-means statistical classification algorithm is used for classification of different process conditions, and quantitative evaluation of the classification performance in terms of cohesion and isolation of the clusters. The identified acoustic signatures demonstrate potential for in-situ monitoring and quality control of the additive manufacturing process and parts. A numerical model of the temperature field and the ultrasonic wave displacement field induced by an incident pulsed laser on additively manufactured stainless steel 17 4 PH is established which is based on thermoelastic theory. The numerical results indicate that the thermoelastic source and the ultrasonic wave features are strongly affected by the characteristics of the laser source and the thermal and mechanical properties of the material. The magnitude and temporal-spatial distributions of the pulsed laser source energy are very important factors which determine not only the wave generation mechanisms, but also the amplitude and characteristics of the resulting elastic wave signals

Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging

Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials and devices. However, the Moore’s Law is expected to stop by 2025 due to reaching the limits of traditional transistor scaling. However, the classical approach has shown to be unable to keep up with the needs of materials manufacturing, requiring more than 20 years to move a material from discovery to market. To adapt materials fabrication to the needs of the 21st century, it is necessary to develop methods for much faster processing of experimental data and connecting the results to theory, with feedback flow in both directions. However, state-of-the-art analysis remains selective and manual, prone to human error and unable to handle large quantities of data generated by modern equipment. Recent advances in scanning transmission electron and scanning tunneling microscopies have allowed imaging and manipulation of materials on the atomic level, and these capabilities require development of automated, robust, reproducible methods.Artificial intelligence and machine learning have dealt with similar issues in applications to image and speech recognition, autonomous vehicles, and other projects that are beginning to change the world around us. However, materials science faces significant challenges preventing direct application of the such models without taking physical constraints and domain expertise into account.Atomic resolution imaging can generate data that can lead to better understanding of materials and their properties through using artificial intelligence methods. Machine learning, in particular combinations of deep learning and probabilistic modeling, can learn to recognize physical features in imaging, making this process automated and speeding up characterization. By incorporating the knowledge from theory and simulations with such frameworks, it is possible to create the foundation for the automated atomic scale manufacturing