A fractional phase-coding strategy for terahertz beam patterning on digital metasurfaces.

Coding metasurfaces have drawn great attention for its digital wave manipulation in deep subwavelength-scale in the last decade, more sophisticated and flexible coding strategies suitable for terahertz wavefront manipulations are becoming more urgently demanded. Due to its rigidity in phase gradient division, both phase gradient metasurfaces and conventional phase coding technique lack the flexibility to expand applications in a large field of view and accurate targeting. This study presents a generalized coding method by precisely reconfiguring the array factor based on the phased array theory and metasurface concept, which can be applied for anomalous scattering and ultrafine radiation patterning. According to our quantitative analysis on the relationship between the deflected angles and the supercell spacing, a fractional coding method for arbitrary phase gradient distribution has been attained by logically discretizing the spacing scale of supercells. By switching on different coding sequences or incident frequencies, a single beam to multiple beam scanning in an expanded angular range with minimal step can be achieved on the fractional phase-coding metasurfaces. As a proof of concept, the 2-bit coding metasurfaces arranged by four fractional coding sequences have been fabricated and measured, demonstrating a consecutive single-beam steering pattern ranging from 22° to 74° in 0.34-0.5 THz. Crosswise verified by the good accordance among numerical prediction, simulation and experiment, the proposed coding strategy paves a path to delicate beam regulation for high-resolution imaging and detection.

Real-time programmable metasurface for terahertz multifunctional wave front engineering.

Terahertz (THz) technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing systems. One of the key challenges facing the field is the need for tools to enable agile engineering of THz wave fronts. Here, we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays architecture. This subwavelength-spaced array, under the control of a 1-bit digital coding sequence, can switch between an enormous range of possible configurations, providing facile access to nearly arbitrary wave front control for signals near 0.34 THz. We demonstrate wide-angle beam scanning with 1° of angular precision over 70 GHz of bandwidth, as well as the generation of multi-beam and diffuse wave fronts, with a switching speed up to 100 MHz. This device, offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene, will open new realms of possibilities in sensing, imaging, and networking.

Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification.

In recent years, deep learning has been applied to many medical imaging fields, including medical image processing, bioinformatics, medical image classification, segmentation, and prediction tasks. Computer-aided detection systems have been widely adopted in brain tumor classification, prediction, detection, diagnosis, and segmentation tasks. This work proposes a novel model that combines the Bayesian algorithm with depth-wise separable convolutions for accurate classification and predictions of brain tumors. We combine Bayesian modeling learning and Convolutional Neural Network learning methods for accurate prediction results to provide the radiologists the means to classify the Magnetic Resonance Imaging (MRI) images rapidly. After thorough experimental analysis, our proposed model outperforms other state-of-the-art models in terms of validation accuracy, training accuracy, F1-score, recall, and precision. Our model obtained high performances of 99.03% training accuracy and 94.32% validation accuracy, F1-score, precision, and recall values of 0.94, 0.95, and 0.94, respectively. To the best of our knowledge, the proposed work is the first neural network model that combines the hybrid effect of depth-wise separable convolutions with the Bayesian algorithm using encoders.

Nanoscale memristor device as synapse in neuromorphic systems.

A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal-oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.

Evolution map of the memristor: from pure capacitive state to resistive switching state.

Memristors possess great application prospects in terabit nonvolatile storage devices, memory-in-logic algorithmic chips and bio-inspired artificial neural network systems. However, "what is the origin state of the memristor?" has remained an unanswered question for half a century. While many applications rely on the memristor, its origin state is becoming a fundamental issue. Herein, we reveal a new state, the pure capacitance state (PCS), which occurs before the memristor is triggered, and the origin state of the memristor can be verified in the memory cells through controlling the ambience parameters. Discovery of the PCS, a missing earlier stage of the memristor, completes the whole evolution map of the memristor from the very beginning to the final developed state.

Dual-band refractometric terahertz biosensing with intense wave-matter-overlap microfluidic channel.

We theoretically and experimentally demonstrate a label-free terahertz biosensor with ultrahigh sensitivity and distinctive discretion. By constructing a metal-air-metal (MAM) metamaterial perfect absorber (MPA) with a metallic paired-ring resonator array, a hollow microfluidic channel, and a backed reflector, a novel dual-band absorptive sensing platform is proposed in the THz range. The near field coupling by dipole-induced trapped modes and the magnetic momentum caused a vertical to transverse power flux that dramatically enhanced the electromagnetic field on top of the metasurface and in the microfluidic channel, respectively. Both the resonant modes exhibit perfect absorption and produce ultrahigh normalized sensitivities of 0.47/RIU (refractive index unit, RIU) and 0.51/RIU at 0.76 THz and 1.28 THz, respectively. Compared with conventional microfluidic sensors, the salient advantages of our design are the perfect spatial overlap for light-matter interaction and polarization insensitivity. Characterized by THz time domain spectroscopic absorption quantification measurements with different concentrations of bovine serum albumin (BSA), the proposed sensor exhibits promising applications in microfluidic biosensing.

Online Supervised Learning for Hardware-Based Multilayer Spiking Neural Networks Through the Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity.

In this paper, we propose an online learning algorithm for supervised learning in multilayer spiking neural networks (SNNs). It is found that the spike timings of neurons in an SNN can be exploited to estimate the gradients that are associated with each synapse. With the proposed method of estimating gradients, learning similar to the stochastic gradient descent process employed in a conventional artificial neural network (ANN) can be achieved. In addition to the conventional layer-by-layer backpropagation, a one-pass direct backpropagation is possible using the proposed learning algorithm. Two neural networks, with one and two hidden layers, are employed as examples to demonstrate the effectiveness of the proposed learning algorithms. Several techniques for more effective learning are discussed, including utilizing a random refractory period to avoid saturation of spikes, employing a quantization noise injection technique and pseudorandom initial conditions to decorrelate spike timings, in addition to leveraging the progressive precision in an SNN to reduce the inference latency and energy. Extensive parametric simulations are conducted to examine the aforementioned techniques. The learning algorithm is developed with the considerations of ease of hardware implementation and relative compatibility with the classic ANN-based learning. Therefore, the proposed algorithm not only enjoys the high energy efficiency and good scalability of an SNN in its specialized hardware but also benefits from the well-developed theory and techniques of conventional ANN-based learning. The Modified National Institute of Standards and Technology database benchmark test is conducted to verify the newly proposed learning algorithm. Classification correct rates of 97.2% and 97.8% are achieved for the one-hidden-layer and two-hidden-layer neural networks, respectively. Moreover, a brief discussion of the hardware implementations is presented for two mainstream architectures.

Properties of spoof plasmon in thin structures.

Spoof surface plasmon polariton (SSPP) is an exotic electromagnetic state that confines light at a subwavelength scale at a design-specific frequency. It has been known for a while that spoof plasmon mode can exist in planar, thin structures with dispersion properties similar to that of its wide three-dimensional structure counterpart. We, however, have shown that spoof plasmons in thin structures possess some unique properties that remain unexplored. Our analysis reveals that the field interior to SSPP waveguide can achieve an exceptional hyperbolic spatial dependence, which can explain why spoof plasma resonance incurs red-shift with the reduction of the waveguide thickness, whereas common wisdom suggests frequency blue-shift of a resonant structure with its size reduction. In addition, we show that strong confinement can be achieved over a wide band in thin spoof plasmon structure, ranging from the spoof plasma frequency up to a lower frequency considerably away from the resonant point. The nature of lateral confinement in thin SSPP structures may enable interesting applications involving fast modulation rate due to enhanced sensitivity of optical modes without compromising modal confinement.

Epidural anesthesia: A safe option for cesarean section in parturient with severe pulmonary hypertension.

Rheumatic heart disease is the most common cardiac disease complicating pregnancy in developing countries. Heart disease accounts for 15% pregnancy-related mortality. In the presence of maternal heart disease, the circulatory changes of pregnancy may result in exacerbation of the hemodynamic perturbations due to complex cardiac valvular lesions leading to decompensation or death of mother or fetus. Determining the ideal anesthetic technique for cesarean section in the presence of complex cardiac conditions remains a much debated topic. General anesthesia is associated with a further increase in pulmonary pressure in response to laryngoscopy and intubation along with myocardial depression by anesthetic agents. Neuraxial blockade may lead to decrease in systemic vascular resistance and cardiac output. We report the successful anesthetic management of a parturient suffering from rheumatic heart disease with multivalvular lesions resulting in severe pulmonary hypertension under epidural anesthesia with good maternal and neonatal outcome. Successful management requires vigilant perioperative monitoring and thorough knowledge of the hemodynamics of complex cardiac valvular disease.

Electrodynamics of spoof plasmons in periodically corrugated waveguides.

States of the electromagnetic field confined near a periodically corrugated surface of a perfect conductor, spoof surface plasmon polaritons (SSPP), are approached systematically based on the developed adaptation of the mode matching technique to the transfer matrix formalism. Within this approach, in the approximation of narrow grooves, systems with arbitrary transversal structure can be investigated straightforwardly, thus lifting the restrictions of the effective medium description and usual implementations of mode matching. A compact expression for the SSPP coupling parameter accounting for the effect of higher Bloch modes is found. The results of the general analysis are applied for studying the effect of dielectric environment on SSPP spectra. It is shown that the effective SSPP plasma frequency is unaffected by the dielectric constant of the medium outside of the grooves and the main effect of sufficiently wide dielectric slabs covering the corrugated surface is described by simple rescaling of the maximal value of the Bloch wavenumber and the coupling parameter. Additionally, in the case of a thin dielectric layer, it is shown that SSPP are sensitive to variation of the thickness of the layer on the sub-wavelength scale.

Dirac electrons in the presence of a matrix potential barrier: application to graphene and topological insulators.

Scattering of 2D Dirac electrons on a rectangular matrix potential barrier is considered using the formalism of spinor transfer matrices. It is shown, in particular, that in the absence of the mass term, the Klein tunneling is not necessarily suppressed but occurs at oblique incidence. The formalism is applied to studying waveguiding modes of the barrier, which are supported by the edge and bulk states. The condition of the existence of the uni-directionality property is found. We show that the band of edge states is always finite with massless excitations, while the spectrum of the bulk states, depending on the parameters of the barrier, may consist of the infinite or finite band with both, massive and massless, low-energy excitations. The effect of the Zeeman term is considered and the condition of the appearance of two distinct energy-dependent directions corresponding to the Klein tunneling is found.

Memristor-based cellular nonlinear/neural network: design, analysis, and applications.

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.

Infected internal jugular vein thrombus in a case of infected arterio-venous fistula for dialysis access.

Infected internal jugular vein (IJV) thrombus is rare and is sometimes seen in association with jugular vein catheterization and rarely with suppurative upper aero-digestive tract infection. We describe a very rare association of left Infected Internal jugular vein thrombus with an infected arterio-venous fistula in the left elbow region created for dialysis access in a renal failure patient. The infected arterio-venous fistula was addressed surgically by excision and a reverse saphenous vein graft was placed between proximal and distal brachial artery just above it's bifurcation. The patient was put on i.v Clindamycin and Metronidazole for six weeks. Patient recovered uneventfully.

Randomized, placebo-controlled trial of granisetron for control of nausea and vomiting during cesarean delivery under spinal anesthesia.

OBJECTIVES: The objective of this study was to evaluate the efficacy and safety of granisetron (5HT3 receptor antagonist) on the incidence of nausea and vomiting in cesarean deliveries under spinal anesthesia. METHODS: In the randomized, double-blind study, 80 parturients received granisetron 40 µg/kg or placebo (n = 40 each) intravenously, immediately after clamping of the fetal umbilical cord. Nausea, vomiting, and adverse events were then observed for 24 h after administration of spinal anesthesia. RESULTS: A complete response (defined as no postoperative nausea and vomiting) during 0-4 h after administration of spinal anesthesia was achieved in 80 % of patients with granisetron and in 45 % of patients with placebo. The corresponding incidences during (4-24 h) were 82.5 and 55 % (P value <0.05). No difference in adverse events was observed in any of the groups. CONCLUSIONS: Prophylactic use of granisetron is effective for preventing emetic episodes during spinal anesthesia for cesarean delivery.