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Point-of-care ultrasound (POCUS) is transforming healthcare worldwide as a diagnostic tool with the potential to significantly reduce the delay between symptom onset and initiation of therapy. Conventional POCUS systems are based on piezoelectric transducers and cable-connected electronics, which require a costly manufacturing process and usually come with an undesirably limited channel count. Such devices typically serve a specific subset of clinical applications, as imaging at different body parts calls for different ultrasound frequencies that are beyond the bandwidth of a single piezoelectric transducer. To enable whole-body imaging, multiple probes with different frequencies, apertures and beamforming (BF) methods are generally required. This further limits the affordability and accessibility of POCUS. Recent advances in micromachined ultrasound transducers (MUTs) have offered an alternative path to addressing these challenges. However, previous attempts to integrate MUTs with chips have been incomplete, neither solving the integration problem [1, 2] nor achieving full ultrasound processing capabilities [3, 4].

Inducing high thermal loads in machining of difficult-to-cut materials changes the mechanical properties of a machined surface/subsurface. In particular, a thermally affected layer leads to tensile residual stresses and microstructure changes. Nickel-based alloys are hard materials and frequently used in different industries. Since the generation of thermal loads in machining Inconel 718 is evident, in this paper an experimental and numerical investigation were performed to evaluate thermal loads and the depth of the affected layer in the machining of Inconel 718 superalloy. First, the effect of cutting parameters was studied on the average machined surface temperature by experimental tests. Then, the results of experiments were used to validate a 3D numerical model. Using the calibration strategy, the heat transfer coefficient at the chip–tool interface was found to be dependent on the cutting conditions. Next, the effect of the initial workpiece hardness and tool geometry, including tool nose ra...

Simulation of large-scale nonlinear dynamical systems on hardware with a high resemblance to their mathematical equivalents has been always a challenge in engineering. This paper presents a novel current-input current-output circuit supporting a systematic synthesis procedure of log-domain circuits capable of computing bilateral dynamical systems with considerably low power consumption and acceptable precision. Here, the application of the method is demonstrated by synthesizing four different case studies: 1) a relatively complex 2-D nonlinear neuron model; 2) a chaotic 3-D nonlinear dynamical system Lorenz attractor having arbitrary solutions for certain parameters; 3) a 2-D nonlinear Hopf oscillator, including bistability phenomenon sensitive to initial values; and 4) three small neurosynaptic networks comprising three FHN neuron models variously coupled with excitatory and inhibitory synapses. The validity of our approach is verified by nominal and Monte Carlo simulated results with realistic process parameters from the commercially available AMS 0.35- $\mu \text{m}$ technology. The resulting continuous-time, continuous-value, and low-power circuits exhibit various bifurcation phenomena, nominal time-domain responses in good agreement with their mathematical counterparts and fairly acceptable process variation results (less than 5% STD).

In recent years, magnetic abrasive finishing (MAF) has become a reliable unconventional technology among researchers in industries due to need for the surface roughness reduction in metals. In this study, experiments based on influential parameters in the MAF process including rotational speed, working gap, and abrasive particle size were designed and conducted in the full factorial method in order to achieve the optimum parameters in finishing of steel AISI 321. A combination of silicon carbide (SiC), iron (Fe), and oil (SAE40) was utilized as magnetic abrasive tool. Prior to the experiments, the surface of the workpiece was abraded to the lowest value of roughness in order to obtain accurate results through the procedure. In general, the results indicate that the parameters of working gap, rotational speed, and abrasive particle size influence the surface roughness from the most to the least, respectively. Indeed, the minimum surface roughness is obtained through working gap of 1 mm, workpiece rotationa...

These days, Trump’s actions have topped regional and world news. His new contracts for the sale of military weapons have created a wave of concern over the growing number of weapons in the world. The Trump administration has been drawing attention since coming in with repeated claims of direct entry into the military. By first proposing a safe zone in Syria, he sought to justify direct US presence in the West Asian region, which made modifications to his plan with Russian and Iranian opposition. Given Washington’s growing difficulty in entering the Syrian military, Trump has chosen a new option for the US body in the region, in which Trump has ordered the use of American drones to bomb various areas of Yemen under the pretext of combating Yemen. Al-Qaeda has given up. The main research question is what is the strategic importance of Yemen in Trump’s foreign and security policy? The findings of the study show that Trump, with goals such as tearing loops of resistance, removing Iran from regional equations and preventing the spread of Shi’ite waves to Saudi Arabia, took a tough and forceful approach to the developments in Yemen. He is reluctant to boycott American power in the region by engaging in showmanship and, with a business-minded and profit-driven view of Yemen, is pouring Western weapons stockpiles with Arab reactionary dollars into the region. The research method is descriptive-analytical based on aggressive realism.

A quadrature synthetic aperture front-end receiver for B-mode ultrasound imaging is presented. The receiver targets small-scale imaging applications such as capsule endoscopy and low-cost portable devices. System complexity, area, power consumption, and cost are minimized using synthetic aperture beamforming (SAB), whereby signals are processed in a sequential manner using only a single channel. SAB is combined with quadrature (I/Q) sampling, which further reduces the bandwidth and computational load. I/Q demodulation is carried out using a full custom analog front-end (AFE), which comprises a low-noise, variable gain preamplifier, followed by a passive mixer, programmable gain amplifier (PGA) and active lowpass filter. A novel preamplifier design is proposed, with quasi-exponential time-gain control and low noise ( ${\text{5.42 nV}}/\sqrt{\text{Hz}}$ input-referred noise). Overall, the AFE consumes ${\text{7.8 mW}}$ (static power) and occupies ${\text{1.5}}\,\text{mm}\times {\text{1.5}}\,\text{mm}$ in AMS ${\text{0.35}}\,\mu \text{m}$ CMOS. Real-time SAB is carried out using a Spartan-6 FPGA, which dynamically apodises and focuses the data by interpolating and applying complex phase rotations to the I/Q samples. For a frame rate of ${\text{7}}\,\text{Hz}$ , the power consumption is ${\text{3.4}}\,\text{mW}/\text{channel}$ across an aperture of 64 elements. B-mode images were obtained using a database of ultrasound signals ( ${\text{2.5}}\,\text{MHz}$ center frequency) derived from a commercial ultrasound machine. The normalized root mean squared error between the quadrature SAB image and the RF reference image was ${\text{13}}\%$ . Image quality/frame rate may be tuned by varying the degree of spatial compounding.

This paper presents a modified astrocyte model that allows a convenient digital implementation. This model is aimed at reproducing relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system. Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte and a biological neuronal network model constructed by connecting two limit-cycle Hopf oscillators to an implementation of the proposed astrocyte model using oscillator-astrocyte interactions with weak coupling. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm that the proposed astrocyte model, with considerably low hardware overhead, can mimic biological astrocyte model behaviors, resulting in desynchronization of the two coupled limit-cycle oscillators.

A real-time control scheme is proposed to correct undesired voltages in distribution networks by adjusting the reactive powers produced by Dispersed Generation Units (DGUs). This is performed at two levels: locally, in DGU controllers, and centrally by a supervisory controller acting on some of the DGUs only. The centralized, discrete controller solves a multi-time-step constrained optimization inspired of Model Predictive Control. Unacceptable voltage deviations are partially but rapidly corrected by the local controls. Further but slower corrections are applied by the upper level, taking advantage of the coordination between DGUs. This hybrid control is of practical interest where the deployment of the upper level control is not feasible or affordable over all DGUs. The proposed method effectiveness is demonstrated on a 75-bus test system.

This paper presents a digital hardware implementation of a frequency adaptive Hopf oscillator along with investigation on systematic behavior when they are coupled in a population. The mathematical models of the oscillator are introduced and compared in sense of dynamical behavior by using system-level simulations based on which a piecewise-linear model is developed. It is shown that the model is capable to be implemented digitally with high efficiency. Behavior of the oscillators in different network structures to be used for dynamic Fourier analysis is studied and a structure with more precise operation which is also more efficient for FPGA-based implementation is implemented. Conceptual block-diagram and a high level representation for this network structure are shown where design process and synthesis are explained based on which physical implementation is demonstrated and tested.

It is expensive to simulate large-scale neural networks on hardware while ensuring a high resemblance to the original neurons' behavior. This paper introduces a novel technique to facilitate digital implementation and computer simulation of neuron models that contain an exponential term. This technique is applied to a biologically realistic neuron model called Adaptive Exponential integrated and fire (AdEx). Hardware synthesis and physical implementations show that the resulting model can reproduce precise neural behavior with high performance and considerably lower implementation costs compared with the original AdEx model.

There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with the original model. Accordingly, a compact structure of the models which can be trained with supervised and unsupervised learning algorithms has been developed. Using this structure and based on a spike rate coding, a character recognition case study has been implemented and tested., Comment: 14 pages, 16 figures

This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuromorphic dynamical systems such as bio-inspired neuron models, and (ii) an efficient mixed analog-digital circuit, which can be conveniently implemented on a hybrid memristor-crossbar/CMOS platform, for hardware implementation of the scheme. This approach employs 4n memristors and no switch for implementing an n-cell system in comparison with 2n2 memristors and 2n switches of a Cellular Memristive Dynamical System (CMDS). Moreover, this approach allows for dynamical variables with both analog and one-hot digital values opening a wide range of choices for interconnections and networking schemes. Dynamical response analyses show that this circuit exhibits various responses based on the underlying bifurcation scenarios which determine the main characteristics of the neuromorphic dynamical systems. Due to high programmability of the circuit, it can be applied to a variety of learning systems, real-time applications, and analytically indescribable dynamical systems. We simulate the FitzHugh-Nagumo (FHN), Adaptive Exponential (AdEx) integrate and fire, and Izhikevich neuron models on our platform, and investigate the dynamical behaviors of these circuits as case studies. Moreover, error analysis shows that our approach is suitably accurate. We also develop a simple hardware prototype for experimental demonstration of our approach., This work was partially funded by EU HBP project under grant number FP7-ICT-2013-FET-F-604102, by EU H2020 ECOMODE project under grant agreement 604102, and by Spanish research grant (with support from the European Regional Development Fund) TEC2012-37868-C04-01 (BIOSENSE).

In this paper we study the neural dynamics and bifurcation analysis of easily implementable piecewise linear (PWL) spiking neuron models with membrane potential variables, input current, recovery variables, and parameters describing timescales. Analyses reveal that the models can reproduce six (all) kinds of bifurcation phenomena that are observed in standard biological neuron models. Moreover, these bifurcations are confirmed by time domain simulations, and along with the different phase plane geometries, these qualitative analyses provide explicit tool for the interpretation of different spiking patterns, and to guide parameter selection in PWL neuron models.