Gesture Based Control and EMG Decomposition

This paper presents two probabilistic developments for use with Electromyograms (EMG). First described is a new-electric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMG into individual motor unit action potentials. This more complex technique will then allow for higher resolution in separating muscle groups for gesture recognition. All examples presented rely upon sampling EMG data from a subject's forearm. The gesture based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models which are used to recognize the gestures as they are being performed in real-time from moving averages of EMG. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMG do not provide easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups we present a Bayesian algorithm to separate surface EMG into representative motor unit action potentials. The algorithm is based upon differential Variable Component Analysis (dVCA) [l], [2] which was originally developed for Electroencephalograms. The algorithm uses a simple forward model representing a mixture of motor unit action potentials as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data was obtained using a custom linear electrode array designed for this study

Molecular beam epitaxial growth and luminescence of InxGa1−xAs/InxAl1−xAs multiquantum wells on GaAs

This letter reports the successful molecular beam epitaxial growth of high‐quality InxGa1−xAs/InxAl1−xAs directly on GaAs. In situ observation of dynamic high‐energy electron diffraction oscillations during growth of InxGa1−xAs on GaAs indicates that the average cation migration rates are reduced due to the surface strain. By raising the growth temperature to enhance the migration rate and by using misoriented epitaxy to limit the propagation of threading and screw dislocations, we have grown device‐quality In0.15Ga0.85As/In0.15Al0.85As multiquantum wells on GaAs with a 0.5–1.0 μm In0.15Ga0.85As buffer layer. The luminescence efficiency of the bound exciton peak increases with misorientation and its linewidth varies from 11 to 15 meV.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69823/2/APPLAB-51-4-261-1.pd

Connectivity of the bay scallop (Argopecten irradians) in Buzzards Bay, Massachusetts, U.S.A.

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Fisheries Oceanography 24 (2015): 364-382, doi:10.1111/fog.12114.The harvest of bay scallops (Argopecten irradians) from Buzzards Bay, Massachusetts, USA undergoes large interannual fluctuations, varying by more than an order of magnitude in successive years. To investigate the extent to which these fluctuations may be due to yearly variations in the transport of scallop larvae from spawning areas to suitable juvenile habitat (settlement zones), a high-resolution hydrodynamic model was used to drive an individual-based model of scallop larval transport. Model results revealed that scallop spawning in Buzzards Bay occurs during a time when nearshore bay currents were principally directed up-bay in response to a persistent southwesterly sea breeze. This nearshore flow results in substantial transport of larvae from lower-bay spawning areas to settlement zones further up-bay. Averaged over the entire bay, the spawning-to-settlement zone connectivity exhibits little interannual variation. However, connectivities between individual spawning and settlement zones vary by up to an order of magnitude. The model results identified spawning areas that have the greatest probability of transporting larvae to juvenile habitat. Because managers may aim to increase scallop populations either locally or broadly, the high-connectivity spawning areas were divided into: 1) high larval retention and relatively little larval transport to adjoining settlement areas, 2) both significant larval retention and transport to more distant settlement areas, and 3) little larval retention but significant transport to distant settlement areas.This project was supported by the Woods Hole Sea Grant through award NA10OAR4170083. All modeling computations were made on the University of Massachusetts at Dartmouth’s (UMD’s) GPGPU cluster, which was acquired with support from NSF award CNS-0959382 and AFOSR DURIP award FA9550-10-1-0354.2016-07-1

Role of strain and growth conditions on the growth front profile of InxGa1−xAs on GaAs during the pseudomorphic growth regime

Theoretical and experimental studies are presented to understand the initial stages of growth of InGaAs on GaAs. Thermodynamic considerations show that, as strain increases, the free‐energy minimum surface of the epilayer is not atomically flat, but three‐dimensional in form. Since by altering growth conditions the strained epilayer can be grown near equilibrium or far from equilibrium, the effect of strain on growth modes can be studied. In situ reflection high‐energy electron diffraction studies are carried out to study the growth modes and surface lattice spacing before the onset of dislocations. The surface lattice constant does not change abruptly from that of the substrate to that of the epilayer at the critical thickness, but changes monotonically. These observations are consistent with the simple thermodynamic considerations presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70448/2/APPLAB-53-8-684-1.pd

The development of a program analysis environment for Ada

A unit level, Ada software module testing system, called Query Utility Environment for Software Testing of Ada (QUEST/Ada), is described. The project calls for the design and development of a prototype system. QUEST/Ada design began with a definition of the overall system structure and a description of component dependencies. The project team was divided into three groups to resolve the preliminary designs of the parser/scanner: the test data generator, and the test coverage analyzer. The Phase 1 report is a working document from which the system documentation will evolve. It provides history, a guide to report sections, a literature review, the definition of the system structure and high level interfaces, descriptions of the prototype scope, the three major components, and the plan for the remainder of the project. The appendices include specifications, statistics, two papers derived from the current research, a preliminary users' manual, and the proposal and work plan for Phase 2

The Forecasting Ability of Correlations Implied in Foreign Exchange Options

This paper evaluates the forecasting accuracy of correlation derived from implied volatilities in dollar-mark, dollar-yen, and mark-yen options from January 1989 to May 1995. As a forecast of realized correlation between the dollar-mark and dollar-yen, implied correlation is compared against three alternative forecasts based on time series data: historical correlation, RiskMetrics' exponentially weighted moving average correlation, and correlation estimated using a bivariate GARCH (1,1) model. At the one-month and three-month forecast horizons, we find that implied correlation outperforms, often significantly, these alternative forecasts. In combinations, implied correlation always incrementally improves the performance of other forecasts, but not the converse; in certain cases historically based forecasts contribute no incremental information to implied forecasts. The superiority of the implied correlation forecast holds even when forecast errors are weighted by realized variances, reflecting correlation's contribution to the dollar variance of a multicurrency portfolio.

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes