Hand Gesture Controlled Drones: An Open Source Library

Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drone's camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed, and hand gesture data sets are open-sourced at project website.Comment: ICDIS 201

Pressure-tuning of the c-f hybridization in Yb metal detected by infrared spectroscopy up to 18 GPa

It has been known that the elemental Yb, a divalent metal at mbient pressure, becomes a mixed-valent metal under external pressure, with its valence reaching ~2.6 at 30 GPa. In this work, infrared spectroscopy has been used to probe the evolution of microscopic electronic states associated with the valence crossover in Yb at external pressures up to 18 GPa. The measured infrared reflectivity spectrum R(w) of Yb has shown large variations with pressure. In particular, R(w) develops a deep minimum in the mid-infrared, which shifts to lower energy with increasing pressure. The dip is attributed to optical absorption due to a conduction c-f electron hybridization state, similarly to those previously observed for heavy fermion compounds. The red shift of the dip indicates that the $c$-$f$ hybridization decreases with pressure, which is consistent with the increase of valence.Comment: 2 pages, to appear in J. Phys. Soc. Jpn. Supp

Metamaterial Polarization Converter Analysis: Limits of Performance

In this paper we analyze the theoretical limits of a metamaterial converter that allows for linear-to- elliptical polarization transformation with any desired ellipticity and ellipse orientation. We employ the transmission line approach providing a needed level of the design generalization. Our analysis reveals that the maximal conversion efficiency for transmission through a single metamaterial layer is 50%, while the realistic re ection configuration can give the conversion efficiency up to 90%. We show that a double layer transmission converter and a single layer with a ground plane can have 100% polarization conversion efficiency. We tested our conclusions numerically reaching the designated limits of efficiency using a simple metamaterial design. Our general analysis provides useful guidelines for the metamaterial polarization converter design for virtually any frequency range of the electromagnetic waves.Comment: 10 pages, 11 figures, 2 table

Exploiting Inter- and Intra-Memory Asymmetries for Data Mapping in Hybrid Tiered-Memories

Modern computing systems are embracing hybrid memory comprising of DRAM and non-volatile memory (NVM) to combine the best properties of both memory technologies, achieving low latency, high reliability, and high density. A prominent characteristic of DRAM-NVM hybrid memory is that it has NVM access latency much higher than DRAM access latency. We call this inter-memory asymmetry. We observe that parasitic components on a long bitline are a major source of high latency in both DRAM and NVM, and a significant factor contributing to high-voltage operations in NVM, which impact their reliability. We propose an architectural change, where each long bitline in DRAM and NVM is split into two segments by an isolation transistor. One segment can be accessed with lower latency and operating voltage than the other. By introducing tiers, we enable non-uniform accesses within each memory type (which we call intra-memory asymmetry), leading to performance and reliability trade-offs in DRAM-NVM hybrid memory. We extend existing NVM-DRAM OS in three ways. First, we exploit both inter- and intra-memory asymmetries to allocate and migrate memory pages between the tiers in DRAM and NVM. Second, we improve the OS's page allocation decisions by predicting the access intensity of a newly-referenced memory page in a program and placing it to a matching tier during its initial allocation. This minimizes page migrations during program execution, lowering the performance overhead. Third, we propose a solution to migrate pages between the tiers of the same memory without transferring data over the memory channel, minimizing channel occupancy and improving performance. Our overall approach, which we call MNEME, to enable and exploit asymmetries in DRAM-NVM hybrid tiered memory improves both performance and reliability for both single-core and multi-programmed workloads.Comment: 15 pages, 29 figures, accepted at ACM SIGPLAN International Symposium on Memory Managemen

Intestinal fungi contribute to development of alcoholic liver disease

This study was supported in part by NIH grants R01 AA020703, U01 AA021856 and by Award Number I01BX002213 from the Biomedical Laboratory Research & Development Service of the VA Office of Research and Development (to B.S.). K.H. was supported by a DFG (Deutsche Forschungsgemeinschaft) fellowship (HO/ 5690/1-1). S.B. was supported by a grant from the Swiss National Science Foundation (P2SKP3_158649). G.G. received funding from the Yale Liver Center NIH P30 DK34989 and R.B. from NIAAA grant U01 AA021908. A.K. received support from NIH grants RC2 AA019405, R01 AA020216 and R01 AA023417. G.D.B. is supported by funds from the Wellcome Trust. We acknowledge the Human Tissue and Cell Research (HTCR) Foundation for making human tissue available for research and Hepacult GmbH (Munich, Germany) for providing primary human hepatocytes for in vitro analyses. We thank Dr. Chien-Yu Lin Department of Medicine, Fu-Jen Catholic University, Taiwan for statistical analysis.Peer reviewedPublisher PD

Evaluation ofthe Middle East and North Africa Land Data Assimilation System

The Middle East and North Africa (MENA) region is dominated by dry, warm deserts, areas of dense population, and inefficient use of fresh water resources. Due to the scarcity, high intensity, and short duration of rainfall in the MENA, the region is prone to hydro climatic extremes that are realized by devastating floods and times of drought. However, given its widespread water stress and the considerable demand for water, the MENA remains relatively poorly monitored. This is due in part to the shortage of meteorological observations and the lack of data sharing between nations. As a result, the accurate monitoring of the dynamics of the water cycle in the MENA is difficult. The Land Data Assimilation System for the MENA region (MENA LDAS) has been developed to provide regional, gridded fields of hydrological states and fluxes relevant for water resources assessments. As an extension of the Global Land Data Assimilation System (GLDAS), the MENA LDAS was designed to aid in the identification and evaluation of regional hydrological anomalies by synergistically combining the physically-based Catchment Land Surface Model (CLSM) with observations from several independent data products including soil-water storage variations from the Gravity Recovery and Climate Experiment (GRACE) and irrigation intensity derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). In this fashion, we estimate the mean and seasonal cycle of the water budget components across the MENA

TESS asteroseismology of the known red-giant host stars HD 212771 and HD 203949

International audienc

Improving Phase Change Memory Performance with Data Content Aware Access

A prominent characteristic of write operation in Phase-Change Memory (PCM) is that its latency and energy are sensitive to the data to be written as well as the content that is overwritten. We observe that overwriting unknown memory content can incur significantly higher latency and energy compared to overwriting known all-zeros or all-ones content. This is because all-zeros or all-ones content is overwritten by programming the PCM cells only in one direction, i.e., using either SET or RESET operations, not both. In this paper, we propose data content aware PCM writes (DATACON), a new mechanism that reduces the latency and energy of PCM writes by redirecting these requests to overwrite memory locations containing all-zeros or all-ones. DATACON operates in three steps. First, it estimates how much a PCM write access would benefit from overwriting known content (e.g., all-zeros, or all-ones) by comprehensively considering the number of set bits in the data to be written, and the energy-latency trade-offs for SET and RESET operations in PCM. Second, it translates the write address to a physical address within memory that contains the best type of content to overwrite, and records this translation in a table for future accesses. We exploit data access locality in workloads to minimize the address translation overhead. Third, it re-initializes unused memory locations with known all-zeros or all-ones content in a manner that does not interfere with regular read and write accesses. DATACON overwrites unknown content only when it is absolutely necessary to do so. We evaluate DATACON with workloads from state-of-the-art machine learning applications, SPEC CPU2017, and NAS Parallel Benchmarks. Results demonstrate that DATACON significantly improves system performance and memory system energy consumption compared to the best of performance-oriented state-of-the-art techniques.Comment: 18 pages, 21 figures, accepted at ACM SIGPLAN International Symposium on Memory Management (ISMM