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The problem of determining the proper call acceptance policy in broadband communication systems to optimize the long-run average revenue per unit time generated by the accepted calls is analyzed. Threshold-type policies that employ the known duration of arriving calls and the current link status are compared. Results show that the interpretable analytical findings for K = 1 can also used for K > 1 using the complete partitioning method. Stochastic optimization methods can be used to calculate optimal thresholds in the general case.

Summary: We report an approach for determining the structure of macromolecular assemblies by the combined application of cryo-electron microscopy (cryo-EM) and site-directed spin labeling electron paramagnetic resonance spectroscopy (EPR). This approach is illustrated for Hsp16.5, a small heat shock protein that prevents the aggregation of nonnative proteins. The structure of Hsp16.5 has been previously studied by both cryo-EM and X-ray crystallography. The crystal structure revealed a roughly spherical protein shell with dodecameric symmetry; however, residues 1–32 were found to be disordered. The cryo-EM reconstruction at 13 Å resolution appeared similar to the crystal structure but with additional internal density corresponding to the N-terminal regions of the 24 subunits. In this study, a systematic application of site-directed spin labeling and EPR spectroscopy was carried out. By combining the EPR constraints from spin label accessibilities and proximities with the cryo-EM density, we obtained an atomic model for a portion of the Hsp16.5 N-terminal region in the context of the oligomeric complex. [Copyright &y& Elsevier]

This paper presents a real-time embedded framework for finger force control of upper extremity prostheses. The proposed system is based on the hypothesis that models describing the finger force and surface Electromyographic (sEMG) signal relationships of healthy subjects can be applied to amputees. An identification/estimation scheme is applied to the collected sEMG and finger force signals in order to infer dynamical models relating the two. A LQG control law is proposed based on this estimation scheme in order to control finger forces of upper extremity prostheses. For the force estimation, filtered sEMG signals from a sensor array and finger force data of a healthy subject are acquired. Real-time estimation and control are implemented using a PIC32MX360F512L microcontroller. In this paper, a novel fusion technique, the Optimized Linear Model Fusion Algorithm (OLMFA) is developed for estimating the skeletal muscle force from the sEMG sensor array in real-time. The sEMG signal is rectified and filtered using a Half-Gaussian filter, and fed to the OLMFA based Multiple Input Single Output (MISO) force model. This MISO system provides the estimated finger force as reference input to the upper extremity prostheses in real-time. A LQG controller is designed to control the finger force of the prostheses utilizing the force estimate from the OLMFA as a reference. Both the OLMFA and the LQG control scheme are prototyped on the embedded framework for testing of the real-time performance. The proposed embedded framework features rate partitioning and UART interface for performance validation and troubleshooting. The OLMFA based force estimation yields a real-time performance of 85.6% mean correlation and 20.4% mean relative error with a standard deviation of ±1.6 and ±1.5 respectively for 18 test subject’s k -fold cross validation data. The LQG control algorithm yields a real-time performance of 91.6% mean correlation and 9.2% mean relative error with a standard deviation of ±1.4 and ±1.3 respectively. [ABSTRACT FROM AUTHOR]

Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed. The sEMG signals are pre-processed using five different filters: Butterworth, Chebychev Type II, Exponential, Half-Gaussian and Wavelet transforms. Dynamic models are extracted from the acquired data using Nonlinear Wiener Hammerstein (NLWH) models and Spectral Analysis Frequency Dependent Resolution (SPAFDR) models based system identification techniques. A detailed comparison is provided for the proposed filters and models using 18 healthy subjects. Wavelet transforms give higher mean correlation of 72.6 7 1.7 (mean 7 SD) and 70.4 7 1.5 (mean 7 SD) for NLWH and SPAFDR models, respectively, when compared to the other filters used in this work. Experimental verification of the fusion based hybrid models with wavelet transform shows a 96% mean correlation and 3.9% mean relative error with a standard deviation of 7 1.3 and 7 0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects' k-fold cross validation data. [ABSTRACT FROM AUTHOR]