NEW INTERPRETATION ON EMG CHARATERISTICS OF SPASTIC CEREBRAL PALSY DURING A REHABILITATIVE WALKING EXERCISE

The purpose of this study is to interpret the EMG characteristics of spastic cerebral palsy children during walking with power spectrum analysis. The EMG signal of 16 cerebral palsy patients (GP) and 18 age matched control (Normal) were collected during several walking trial. It was found that our CP participants ha:d significantly longer firing duration and higher median frequency within a gait cycle for al.l the muscle groups, these indicated of the EMG characteristics of in the spastic muscles. In addition, the CP produced significantly smaller root mean square value in tibialis anterior muscle than the normal; this indicated that the tibialis anterior muscle of GP was weakness or atrophy. Because of good objectivity and reproducibility, employing RMS and the MF could :be suggested to be the parameters for further gait studies

Optimal matching between spatial datasets under capacity constraints

Consider a set of customers (e.g., WiFi receivers) and a set of service providers (e.g., wireless access points), where each provider has a capacity and the quality of service offered to its customers is anti-proportional to their distance. The Capacity Constrained Assignment (CCA) is a matching between the two sets such that (i) each customer is assigned to at most one provider, (ii) every provider serves no more customers than its capacity, (iii) the maximum possible number of customers are served, and (iv) the sum of Euclidean distances within the assigned provider-customer pairs is minimized. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between the customer and provider sets. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop incremental techniques that maintain an optimal assignment (in the presence of updates) with a processing cost several times lower than CCA recomputation from scratch. Finally, we present approximate (i.e., suboptimal) CCA solutions that provide a tunable trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques. © 2010 ACM.postprin

Re-factoring based program repair applied to programming assignments

Automated program repair has been used to provide feedback for incorrect student programming assignments, since program repair captures the code modification needed to make a given buggy program pass a given test-suite. Existing student feedback generation techniques are limited because they either require manual effort in the form of providing an error model, or require a large number of correct student submissions to learn from, or suffer from lack of scalability and accuracy. In this work, we propose a fully automated approach for generating student program repairs in real-time. This is achieved by first re-factoring all available correct solutions to semantically equivalent solutions. Given an incorrect program, we match the program with the closest matching refactored program based on its control flow structure. Subsequently, we infer the input-output specifications of the incorrect program's basic blocks from the executions of the correct program's aligned basic blocks. Finally, these specifications are used to modify the blocks of the incorrect program via search-based synthesis. Our dataset consists of almost 1,800 real-life incorrect Python program submissions from 361 students for an introductory programming course at a large public university. Our experimental results suggest that our method is more effective and efficient than recently proposed feedback generation approaches. About 30% of the patches produced by our tool Refactory are smaller than those produced by the state-of-art tool Clara, and can be produced given fewer correct solutions (often a single correct solution) and in a shorter time. We opine that our method is applicable not only to programming assignments, and could be seen as a general-purpose program repair method that can achieve good results with just a single correct reference solution

Modeling of the current density distribution under surface posterior-tibial-nerve electric stimulator

Stimulation of the posterior tibial nerve is commonly used in the measurement of somatosensory evoked potential (SEP). To improve the efficiency of stimulation, the potential field and current density distributions under the surface electrodes were modeled and simulated. In our model, three layers were assumed: (1) the air environment, (2) electrode and paste (3) human body (skin and soft tissues). The mirror method was used to analyze the potential field of point charge. Integration of the field and the area of the stimulus gave the potential field of one surface electric pole. The potential field distribution of the bipolar stimulator was obtained by superimposition of two unipolar fields. Finally, the current density distribution was calculated by Laplace equation. The analytical solution of the potential field was found and the numerical solution of the current density distribution calculated. The potential field and current density distributions were simulated by 2-D plot. From the model and simulation, the potential and current density distributions were not found to be uniform under transcutaneous stimulation electrode and the maximum current density is located under the poles. We recommend that bipolar stimulator should be applied axially along the stimulated nerve course.published_or_final_versio

A Unified Quantum NOT Gate

We study the feasibility of implementing a quantum NOT gate (approximate) when the quantum state lies between two latitudes on the Bloch's sphere and present an analytical formula for the optimized 1-to-$M$ quantum NOT gate. Our result generalizes previous results concerning quantum NOT gate for a quantum state distributed uniformly on the whole Bloch sphere as well as the phase covariant quantum state. We have also shown that such 1-to-$M$ optimized NOT gate can be implemented using a sequential generation scheme via matrix product states (MPS)

Broadband 10 Gb/s operation of graphene electro-absorption modulator on silicon

High performance integrated optical modulators are highly desired for future optical interconnects. The ultrahigh bandwidth and broadband operation potentially offered by graphene based electro-absorption modulators has attracted a lot of attention in the photonics community recently. In this work, we theoretically evaluate the true potential of such modulators and illustrate this with experimental results for a silicon integrated graphene optical electro-absorption modulator capable of broadband 10 Gb/s modulation speed. The measured results agree very well with theoretical predictions. A low insertion loss of 3.8 dB at 1580 nm and a low drive voltage of 2.5 V combined with broadband and athermal operation were obtained for a 50 mu m-length hybrid graphene-Si device. The peak modulation efficiency of the device is 1.5 dB/V. This robust device is challenging best-in-class Si (Ge) modulators for future chip-level optical interconnects

Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. Results: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data

Wicking and evaporation of liquids in porous wicks: a simple analytical approach to optimization of wick design

Wicking and evaporation of volatile liquids in porous, cylindrical wicks is investigated where the goal is to model, using simple analytical expressions, the effects of variation in geometrical parameters of a wick, such as porosity, height and bead-size, on the wicking and evaporation processes, and find optimum design conditions. An analytical sharp-front flow model involving the single-phase Darcy’s law is combined with analytical expressions for the capillary suction pressure and wick permeability to yield a novel analytical approach for optimizing wick parameters. First, the optimum beadradius and porosity maximizing the wicking flow-rate are estimated. Later, after combining the wicking model with evaporation from the wick-top, the allowable ranges of bead-radius, height and porosity for ensuring full saturation of the wick are calculated. The analytical results are demonstrated using some highly volatile alkanes in a polycarbonate sintered wick