Residual Stress Relaxation Induced by Mass Transport Through Interface of the Pd/SrTiO3

Metal interconnections having a small cross-section and short length can be subjected to very large mass transport due to the passing of high current densities. As a result, nonlinear diffusion and electromigration effects which may result in device failure and electrical instabilities may be manifested. Various thicknesses of Pd were deposited over SrTiO3 substrate. Residual stress of the deposited film was evaluated by measuring the variation of d-spacing versus sin2ψ through conventional X-ray diffraction method. It has been found that the lattice misfit within film and substrate might be relaxed because of mass transport. Besides, the relation between residual intrinsic stress and oxygen diffusion through deposited film has been expressed. Consequently, appearance of oxide intermediate layer may adjust interfacial characteristics and suppress electrical conductivity by increasing electron scattering through metallic films

Explainable AI-powered Graph Neural Networks for HD EMG-Based Gesture Intention Recognition

The ability to recognize fine-grained gestures enables several applications in different domains, including healthcare, robotics, remote control, and human-computer interaction. Traditional gesture recognition systems rely on data acquired from cameras, depth sensors, or smart gloves. More recently, techniques for recognizing gestures based on signals acquired by high-density (HD) EMG electrodes worn on the forearm have been proposed. An advantage of these techniques is that they do not rely on the use of external devices, and they are feasible also to people who underwent amputation. Unfortunately, the extraction of complex features from raw HD EMG signals may introduce delays that deter the real-time requirements of the system. To address this issue, in a preliminary investigation we proposed to use graph neural networks for gesture recognition from raw HD EMG data. In this paper, we extend our previous work by exploiting Explainable AI algorithms to automatically refine the graph topology based on the data in order to improve recognition rates and reduce the computational cost. We performed extensive experiments with a large dataset collected from 20 volunteers regarding the execution of 65 fine-grained gestures, comparing our technique with state-of-the-art methods based on handcrafted features and different machine learning algorithms. Experimental results show that our technique outperforms the state of the art in terms of recognition performance while incurring significantly lower computational cost at run-time

Event Stream Processing with Multiple Threads

Current runtime verification tools seldom make use of multi-threading to speed up the evaluation of a property on a large event trace. In this paper, we present an extension to the BeepBeep 3 event stream engine that allows the use of multiple threads during the evaluation of a query. Various parallelization strategies are presented and described on simple examples. The implementation of these strategies is then evaluated empirically on a sample of problems. Compared to the previous, single-threaded version of the BeepBeep engine, the allocation of just a few threads to specific portions of a query provides dramatic improvement in terms of running time

Reducing motor variability enhances myoelectric control robustness across untrained limb positions

The limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. Factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased input data variability. This variability can cause incorrect decoding of user intent. Previous research has attempted to address this by better capturing input data variability with data abundance. In this paper, we take an alternative approach and investigate the effect of reducing trial-to-trial variability by improving the consistency of muscle activity through user training. Ten participants underwent 4 days of myoelectric training with either concurrent or delayed feedback in a single arm position. At the end of training participants experienced a zero-feedback retention test in multiple limb positions. In doing so, we tested how well the skill learned in a single limb position generalized to untrained positions. We found that delayed feedback training led to more consistent muscle activity across both the trained and untrained limb positions. Analysis of patterns of activations in the delayed feedback group suggest a structured change in muscle activity occurs across arm positions. Our results demonstrate that myoelectric user-training can lead to the retention of motor skills that bring about more robust decoding across untrained limb positions. This work highlights the importance of reducing motor variability with practice, prior to examining the underlying structure of muscle changes associated with limb position

Fetal Electrocardiogram Signal Modelling Using Genetic Algorithm

The fetal electrocardiogram signal (FECG) is a major tool in monitoring and diagnosis of the fetus high risk conditions and arrhythmias. This paper introduces an accurate mathematical model for fetal electrocardiogram based on a real template FECG signal. This is done after elimination of maternal electrocardiogram (MECG) signal interference measured during pregnancy. The parameters of the introduced model are optimized in terms of sum square error using a genetic algorithm (GA). Our precise fetal electrocardiogram model can be a benchmark for other prospective researches particularly in FECG extraction. ©2007 IEEE

Greater intermanual transfer in the elderly suggests age-related bilateral motor cortex activation is compensatory

ABSTRACT. Hemispheric lateralization of movement control diminishes with age; whether this is compensatory or maladaptive is debated. The authors hypothesized that if compensatory, bilateral activation would lead to greater intermanual transfer in older subjects learning tasks that activate the cortex unilaterally in young adults. They studied 10 young and 14 older subjects, learning a unimanual visuomotor task comprising a feedforward phase, where there is unilateral cortical activation in young adults, and a feedback phase, which activates the cortex bilaterally in both age groups. Increased intermanual transfer was demonstrated in older subjects during feedforward learning, with no difference between groups during feedback learning. This finding is consistent with bilateral cortical activation being compensatory to maintain performance despite declining computational efficiency in neural networks

Incoherent dictionary pair learning : application to a novel open-source database of chinese numbers

We enhance the efficacy of an existing dictionary pair learning algorithm by adding a dictionary incoherence penalty term. After presenting an alternating minimization solution, we apply the proposed incoherent dictionary pair learning (InDPL) method in classification of a novel open-source database of Chinese numbers. Benchmarking results confirm that the InDPL algorithm offers enhanced classification accuracy, especially when the number of training samples is limited