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The L1-norm regularization is applied to ultrasonic reverberation characteristics imaging and vectoral Doppler measurement, of which performances are compared with those of L2-norm regularizations. The L1 regularization yields the sharper image than the L2 regularization. Alternatively, for the Doppler measurement, the L1 regularization yields less accuracy than the L2 regularization. Clinical Relevance- This study will permit us to perform quantitative ultrasonic reverberation characteristics and accurate vectoral Doppler observation.

In this paper, a novel ergodic cellular automaton model of Hes1 mRNA and Hes1 protein network is presented. Detailed analyses reveal that the presented network model can reproduce a typical nonlinear bifurcation phenomenon observed in a conventional delay differential equation model of the Hes1 mRNA and Hes1 protein network. Furthermore, the presented network model is implemented by a field programmable gate array and its operation is validated by experiments. It is shown that the presented network model consumes much fewer circuit elements and much lower power compared to the delay differential equation network model. Hence the results of this paper will provide fundamental knowledge to design an efficient hardware-based gene network simulator.

In the last years the spread of hand prosthetics has fueled the research on the field of signal processing applied on physiologic data. At the state of the art there are different algorithms that allow a precise estimation of hand movements, the majority of whom work just on the electrode space. Even though there are signal processing methods that access single muscle information, they are still premature for a real application on prosthetics. We present a novel method that exploit the information extracted from a magnetic resonance image (MRI) and a single row of high-density surface electromyography (HD-sEMG) electrodes to estimate the muscles currents in the forearm, providing a first experimental application on two simple wrist movements to assess its performance. The results show that the proposed method is able to identify the correct muscle with a single muscle-contraction task, whereas for a 2 muscle task it shows a high variance in the results. The method models the signal propagation from muscles to electrodes using a simple resistive electrical network and uses the graph theory to calculate the muscle currents. It brings a considerably simpler muscle's current estimation method, significantly decreasing the problem complexity, and therefore becoming a potential effective approach for future prosthetics' control.