Multilevel Phase Research Articles

A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal.

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.

Quantitative Phase Retrieval Through Scattering Medium via Compressive Sensing

Scattering media, such as biological tissues and turbid liquids, scatter light randomly and introduce several challenges when imaging objects behind them. The transmission matrix (TM) describes the relation between the input and output of a beam transmitted through a medium, which can be used to reconstruct a target located behind a scattering medium. However, the current TM methods cannot easily retrieve the phase distribution of objects inside or behind a scattering medium. In this work, a compressive sensing (CS) method to identify the TM of a scatter contained in an imaging system was investigated. By calibrating the TM, the phase information of the object can be retrieved quantitatively. This method allows one to retrieve multilevel and dynamic phase objects behind different scatters. The influence of the calibration parameters on the reconstruction quality was investigated in detail. The proposed method, featuring noninterference measurements of the TM and exploiting a large field of view, can be used in phase imaging applications.

Invited Paper] Enhanced Recording Density via Multilevel Phase Retrieval and Correlation-based Multiplexed Recording in Computer-generated Holographic Data Storage

Invited Paper] Enhanced Recording Density via Multilevel Phase Retrieval and Correlation-based Multiplexed Recording in Computer-generated Holographic Data Storage

Ultimate phase sensitivity in surface plasmon resonance sensors by tuning critical coupling with phase change materials

Surface plasmon resonance phase interrogation is assumed to be more sensitive than intensity or wavelength interrogation. We propose for the first time theoretical and numerical studies of the phase in surface plasmon resonance sensor and establish a quantitative relation between phase sensitivity and wavelength interrogation. We reveal the link between ultra-high phase sensitivity and critical coupling condition. However, reaching this condition requires a technically infeasible angstrom-level precision in the metal layer thickness. We propose a robust solution to overcome this limitation by coupling the SPR with a phase-change material (PCM) thin film. By exploiting the multilevel reconfigurable phase states of PCM, we theoretically demonstrate ultra-high phase sensitivities with a limit of detection as low as 10−10 refractive index unit (RIU). Such a PCM-assisted SPR sensor platform paves the way for unprecedented sensitivity sensors for the detection of trace amounts of low molecular weight species in biomedical sensing and environmental monitoring.

A scheme for simulating multi-level phase change photonics materials

Chalcogenide phase change materials (PCMs) have been extensively applied in data storage, and they are now being proposed for high resolution displays, holographic displays, reprogrammable photonics, and all-optical neural networks. These wide-ranging applications all exploit the radical property contrast between the PCMs’ different structural phases, extremely fast switching speed, long-term stability, and low energy consumption. Designing PCM photonic devices requires an accurate model to predict the response of the device during phase transitions. Here, we describe an approach that accurately predicts the microstructure and optical response of phase change materials during laser induced heating. The framework couples the Gillespie Cellular Automata approach for modelling phase transitions with effective medium theory and Fresnel equations. The accuracy of the approach is verified by comparing the PCM’s optical response and microstructure evolution with the results of nanosecond laser switching experiments. We anticipate that this approach to simulating the switching response of PCMs will become an important component for designing and simulating programmable photonics devices. The method is particularly important for predicting the multi-level optical response of PCMs, which is important for all-optical neural networks and PCM-programmable perceptrons.

Power-balanced hybrid optics boosted design for achromatic extended depth-of-field imaging via optimized mixed OTF.

A power-balanced hybrid optical imaging system has a diffractive computational camera, introduced in this paper, with image formation by a refractive lens and multilevel phase mask (MPM). This system provides a long focal depth with low chromatic aberrations thanks to MPM and a high energy light concentration due to the refractive lens. We introduce the concept of optical power balance between the lens and MPM, which controls the contribution of each element to modulate the incoming light. Additional features of our MPM design are the inclusion of the quantization of the MPM's shape on the number of levels and the Fresnel order (thickness) using a smoothing function. To optimize the optical power balance as well as the MPM, we built a fully differentiable image formation model for joint optimization of optical and imaging parameters for the proposed camera using neural network techniques. We also optimized a single Wiener-like optical transfer function (OTF) invariant to depth to reconstruct a sharp image. We numerically and experimentally compare the designed system with its counterparts, lensless and just-lens optical systems, for the visible wavelength interval (400-700) nm and the depth-of-field range (0.5-∞ m for numerical and 0.5-2 m for experimental). We believe the attained results demonstrate that the proposed system equipped with the optimal OTF overcomes its counterparts--even when they are used with optimized OTF--in terms of the reconstruction quality for off-focus distances. The simulation results also reveal that optimizing the optical power balance, Fresnel order, and the number of levels parameters are essential for system performance attaining an improvement of up to 5 dB of PSNR using the optimized OTF compared to its counterpart lensless setup.

Broadband achromatic metalens and meta-deflector based on integrated metasurface

Metasurfaces have attracted much attention due to their great ability to manipulate the electromagnetic field over an ultra-compact profile, which facilities the realization of the next generation diffractive optical elements. However, the broadband performance of the metasurface-based optical devices is still subject to the chromatic aberration. Although the broadband achromatic metalenses have recently been studied, the diameters and numerical apertures of the metalens are still limited by the requirement of large phase compensation. Such large phase dispersion is usually realized by sophisticated nanostructures of extremely high aspect ratios that are not easy to fabricate. Here we propose a new kind of metalens by integrating the dielectric pillars with a multi-level phase plate, and achromatic focusing and imaging can be achieved in the wavelength range from 1100 to 1500 nm. In addition, a broadband achromatic meta-deflector is also proposed, which could refract broadband incident light with the same refractive angle. Our integrated metasurface can provide large phase compensation while alleviating the aspect ratios of the composing nanopillars, and shows potential applications in designing arbitrary metadevices with dispersion engineering properties.

Novel automated PD detection system using aspirin pattern with EEG signals

Background and objectiveParkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals. MethodIn this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting. ResultsA public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively. ConclusionOur developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.

Electrical Properties of Sandwich-like Multilevel Phase Structure BNT-BT Lead-Free Piezoelectric Ceramics

Sandwich-like multilayer (BNT-0.05BT, BNT-0.07BT, and BNT-0.09BT) composite ceramics was performed by traditional solid-state reaction method. The results show that the dielectric properties of (Bi1/2Na1/2)TiO3-BaTiO3 (BNT-BT) ceramics were improved significantly from 580 to 1278. By fitting the dielectric constant ɛ r with electric field intensity, the linear relationship conformed to Rayleigh formula, and the dielectric properties of the loops were studied. The results show that the dielectric properties were improved mainly because the intrinsic contribution of structural changes of composite ceramics increased. As the temperature rises, the dielectric response of ceramics to the electric field increases gradually.

Multi-level phase-change memory with ultralow power consumption and resistance drift

By controlling the amorphous-to-crystalline relative volume, chalcogenide phase-change memory materials can provide multi-level data storage (MLS), which offers great potential for high-density storage-class memory and neuro-inspired computing. However, this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase (“drift”) in the amorphous phase, which limits the number of attainable storage levels. Here, we report a new type of MLS system in yttrium-doped antimony telluride, utilizing reversible multi-level phase transitions between three states, i.e., amorphous, metastable cubic and stable hexagonal crystalline phases, with ultralow power consumption (0.6–4.3 pJ) and ultralow resistance drift for the lower two states (power-law exponent < 0.007). The metastable cubic phase is stabilized by yttrium, while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb atoms. Finally, the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high performance. This study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.

Crystallization Properties of Al-Sb Alloys for Phase Change Memory Applications

Material properties of Al-Sb binary alloy thin films deposited under ultra-high vacuum conditions were studied for multi-level phase change memory applications. Crystallization of this alloy was shown to occur in the temperature range of 180 °C–280 °C, with activation energy >2 eV. X-ray diffraction (XRD) from annealed alloy films indicates the formation of two crystalline phases, (i) an Al-doped A7 antimony phase, and (ii) a stable cubic AlSb phase. In-situ XRD analysis of these films show the AlSb phase crystalizes at a much higher temperature as compared to the A7 phase after annealing of the film to 650 °C. Mushroom cell structures formed with Al-Sb alloys on 120 nm TiN heater show a phase change material resistance switching behavior with reset/set resistance ratio >1000 under pulse measurements. TEM and in situ synchrotron XRD studies indicate fine nucleation grain sizes of ∼8–10 nm, and low elemental redistribution that is useful for improving reliability of the devices. These results indicate that Te-free Al-Sb binary alloys are possible candidates for analog PCM applications.

Analytical Models for the Computation of Error Probability of Multi-Level Phase Shift Keying Modulation Scheme

In this paper, analytical models for the computation of error probability (BER) of the Multi-level Phase Shift Keying (MPSK) modulation scheme is presented. Analytical models for computing MPSK bit error probability based on Q function, error function (erf) and complementary error function (erfc) are presented. Also, an analytical model for computing the symbol error rate for MPSK is presented. Furthermore, a generalized analytical expression for BER as a function of modulation order (M) and energy per bit to noise power density ratio (Eb/No) is presented. The BER was computed for various values of M (2 ≤ M ≤ 256) and Eb/No (0 dB ≤ Eb/No ≤ 14 Db). The results showed that at Eb/No =12 dB, a BER of 9.006E-09 is realized for M =2 and M =4 whereas BER of 1.056E-01 is realized for M = 256. Also, for the same M = 2 , the value of BER decreased from 1.2501E-02 at Eb/No = 4 dB to 9.0060E-09at Eb/No =12 dB. Generally, the results showed that for the MPSK modulation scheme, for a given value of Eb/No, the lower modulation order (M) has a lower BER and for a given modulation order, (M) the BER decreases as Eb/No increases.

Deconstructing Multiteam System Action: Development and Content Validation of a Multilevel Multiteam System Action Taxonomy

ABSTRACT Current theories of goal-directed action in networks of closely collaborating teams, have yet to provide a framework that can assist in identifying specific points of multiteam system (MTS) action that led to performance breakdowns. Expanding upon previous frameworks, we develop an MTS action taxonomy detailing 29 within-team alignment and between-team behaviors that occur during the action phase of MTS performance. The taxonomy is built upon our model of MTS action subphases which delineates MTS action into three interrelated, multilevel phases: acting, monitoring, and recalibrating. We evaluate the content validity of the taxonomy using subject matter expert interviews and case studies within a US Navy context. We discuss implications regarding the applicability of the MTS action subphase framework and associated taxonomy for diagnostic and training purposes.

NOVEL MULTI-LEVEL ASPECT BASED SENTIMENT ANALYSIS FOR IMPROVED ROOT-CAUSE ANALYSIS

Aspect extraction and sentiment identification are the two important tasks to provide effective root cause analysis. This work presents a Multi-Level Aspect based Sentiment Analysis (MLASA) model that integrates the aspect extraction and sentiment identification modules to provide effective root cause analysis. The aspect extraction module performs token filtration, followed by rule based aspect identification. The heterogeneous multi-level sentiment identification phase performs aspect based sentiment identification. First level performs magnitude along and polarity identification of text, while the second level performs polarity identification using multiple machine learning models. The results are aggregated and ranked based on aspect significance and sentiment magnitude. Experiments and comparisons show effective performance of the MLASA model.

Multilevel level Single Phase Inverter Implementation for Reduced Harmonic Contents

Selective harmonic elimination technique SHE is adopted in this work to reduce the harmonic contents in single phase cascaded multilevel inverter. The firing instants for the electronic switches MOSFETs in the inverter are calculated off line for five level to thirteen level inverter. An Arduino microcontroller is programmed to cope with different topologies of the multilevel inverter. The implemented multi-level (MLI) inverter results are compared with Simulink simulation program and are found very close to each other. SHE technique works at system frequency (50Hz or 60Hz) and the switching losses are very small. The sinusoidal pulse width modulation SPWM requires a carrier frequency not less 20 times the system frequency so SHE approach is found to be superior compared with SPWM. Also, SHE technique shows significant reduction in THD as the number of levels increased. Results for the output voltages and currents along with their frequency spectrum are shown and compared with traditional SPWM.

Three Resistance States Achieved by Nanocrystalline Decomposition in Ge‐Ga‐Sb Compound for Multilevel Phase Change Memory

AbstractPhase‐change memory (PCM), using the fast and reversible transition between crystal and glass to store binary data, is a promising candidate for next‐generation information storage and computing technologies. Recording more than one bit of information on each memory cell, known as multilevel cell (MLC) technology, can greatly increase the data density of PCMs. In this paper, the MLC capability in a phase change material Ge‐Ga‐Sb (GGS) is explored. Using the “SET” operation with increasing voltage amplitudes on this PCM cell in a 250 nm pillar structure device, three resistance levels are achieved and can be stabilized within large operating voltage windows, allowing large tolerance of SET voltage variation which may lead to the overlap of the neighboring resistance levels. It is discovered that the additional resistance level other than “0” and “1” is enabled by compositional phase separation in the nanocrystalline GGS, as revealed via atom probe tomography and electronic microscopy. The device also shows small resistance drift (ν = 0.025) in amorphous state, about four times lower than prototypical Ge‐Sb‐Te‐based PCMs, stabilizing it against time variation. Meanwhile, high thermal stability (Tc ≈ 300 °C) in GGS‐based device can also facilitate the practical applications in some extreme conditions.

Design and evaluation of 2.3 Tb/s (23 ch. × 100 Gb/s) multi-carrier WDM optical transmission systems

Multilevel phase modulation formats with coherent detection enable a new generation of high speed optical transport platform. We demonstrate 23 × 100 Gbps (2.3 Tb/s) WDM Optical systems employing multicarrier generation concept. Three lasers are used to generate 23 sub-carriers using multicarrier generator at transmitter section. Each sub-carrier transmits at a transmission rate of 100 Gbps using polarization division multiplexed quadrature phase shift keying (PDM-QPSK) modulation format with spacing of 25 GHz between sub carriers. The analysis is carried out for 23 × 100 Gb/s (2.3 Tb/s) PDM-QPSK WDM optical systems with ultra large area fiber (ULAF), standard single mode fiber (SSMF) and large effective area fiber (LEAF) at transmission reach from 600 to 5000 km. Coherent detection with DSP is used for all the systems at receiving end. Systems are verified with parameters like symbol error rate, error vector magnitude (EVM) and Q-factor using analytical and simulation results. Improvement of 1.66 dB to 3.21 dB and 2.08 dB to 4.63 dB is noted in Q-factor for system with ULAF in comparison with system using SSMF and LEAF respectively over 600 km to 5000 km transmission reach. EVM values are found 2.24% to 2.68% and 2.68% to 12.43% lower in system with ULAF than system with SSMF and LEAF respectively. Log of estimated symbol error is observed less for system with ULAF than systems with SSMF and LEAF fibers, respectively, at transmission distance of 5000 km. The results clearly show that WDM optical system with ULAF fiber gives better performance.

Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use

This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN.

Multilevel level single phase inverter implementation for reduced harmonic contents

&lt;p&gt;Selective harmonic elimination technique SHE is adopted in this work to reduce the harmonic contents in single phase cascaded multilevel inverter. The firing instants for the electronic switches MOSFETs in the inverter are calculated off line for five level to thirteen level inverter. An Arduino microcontroller is programmed to cope with different topologies of the multilevel inverter. The implemented multi-level (MLI) inverter results are compared with Simulink simulation program and are found very close to each other. SHE technique works at system frequency (50 Hz or 60 Hz) and the switching losses are very small. The sinusoidal pulse width modulation SPWM requires a carrier frequency not less 20 times the system frequency so SHE approach is found to be superior compared with SPWM. Also, SHE technique shows significant reduction in THD as the number of levels increased. Results for the output voltages and currents along with their frequency spectrum are shown and compared with traditional SPWM.&lt;/p&gt;

Jitter Modeling in Digital CDR with Quantization Noise Analysis

Phase rotator-based digital clock and data recovery (CDR) using multi-level bang-bang phase detector (ML-BBPD) and time to digital converter (TDC) is analyzed at system and circuit level. A model is proposed for calculating the quantization noise and bit error rate (BER), in order to evaluate the important parameters in CDR design. The jitter analysis is done based on the probability density function achieved from the quantization noise error of the BBPD and TDC. The analysis of ML-BBPD is shown that by increasing the number of sampling clocks, the quantization noise and consequently the jitter and BER are significantly reduced. Also, it is shown that by improving the resolution of the TDC and increasing number of delay cells for the purpose of keeping fixed the dynamic range, the output jitter of TDC is decreased. In the proposed model and also simulation, it is approved that by increasing the ratio of RMS input Gaussian jitter to the quantization step, the output jitter reaches to its saturated value. To prove the jitter model, the goodness of fit test based on Kolmogorov–Smirnov test is used and in addition, the simulation is provided for circuit level CDR. The circuit level simulation is done in TSMC 65 nm CMOS technology. The CDR is worked under 1 V supply voltage at 480Mbit/s bit rate useful for USB2 applications. The CDR dissipates 913 µW power and generates 0.258 ps RMS jitter, while it occupies 166 µm × 104 µm chip area.