Advanced Schemes Research Articles

Template Attack Based on the Weighted Quadruple Residual Network

ABSTRACTTemplate attack, as a classic side‐channel attack method, has attracted extensive attention due to its ease of deployment and small number of hyperparameters. However, traditional template attacks (TA) suffer from high preprocessing complexity and poor performance in certain masked applications. To address these issues, this paper proposes a novel template attack method leveraging a weighted quadruple residual network. Specifically, we improve the quadruple network model by assigning different weights to the negative samples selected by the model, which balances the influence of the samples on the model's feedback. Aiming at the fact that the quadruple network ignores the label characteristics of samples in the side‐channel, the distance metric in the quadruple network model is improved, which can help the model select more suitable samples. The residual network designed in this paper enables the quadruplet network model to reduce feature dimensionality, thereby facilitating more effective analysis attacks in TA. Experimental results demonstrate a notable reduction in leaked traces required by the optimized template attack on the synchronous ASCAD dataset, achieving a 62.5% decrease compared to an advanced template attack scheme. On the AES_HD dataset, the energy trace needed for a successful attack diminishes by 46.5% in comparison to the advanced template attack scheme.

Expanded Functionality and Portability for the Colvars Library.

Colvars is an open-source C++ library that provides a modular toolkit for collective-variable-based molecular simulations. It allows practitioners to easily create and implement descriptors that best fit a process of interest and to apply a wide range of biasing algorithms in collective variable space. This paper reviews several features and improvements to Colvars that were added since its original introduction. Special attention is given to contributions that significantly expanded the capabilities of this software or its distribution with major MD simulation packages. Collective variables can now be optimized either manually or by machine-learning methods, and the space of descriptors can be explored interactively using the graphical interface included in VMD. Beyond the spatial coordinates of individual molecules, Colvars can now apply biasing forces to mesoscale structures and alchemical degrees of freedom and perform simulations guided by experimental data within ensemble averages or probability distributions. It also features advanced computational schemes to boost the accuracy, robustness, and general applicability of simulation methods, including extended-system and multiple-walker adaptive biasing force, boundary conditions for metadynamics, replica exchange with biasing potentials, and adiabatic bias molecular dynamics. The library is made available directly within the main distributions of the academic software GROMACS, LAMMPS, NAMD, Tinker-HP, and VMD. The robustness of the software and the reliability of the results are ensured through the use of continuous integration with a test suite within the source repository.

Comparative analysis of RNN versus IIR digital filtering to optimize resilience to dynamic perturbations in pH sensing for vertical farming

AbstractVertical farming (VF) refers to systems of agriculture where crops are grown in trays stacked vertically by exposing them to artificial light and using sensing technology to improve product quality and yield. In this work, we propose an advanced filtering scheme based on recurrent neural networks (RNNs) and deep learning to enable efficient control strategies for VF applications. We demonstrate that the best RNN model incorporates five neuron layers, with the first and second containing 90 long short‐term memory neurons. The third layer implements one gated recurrent units neuron. The fourth segment incorporates one RNN network, while the output layer is designed by using a single neuron exhibiting a rectified linear activation function. By utilizing this RNN digital filter, we introduce two variations: (1) a scaled RNN model to tune the filter to the signal of interest, and (2) a moving average filter to eliminate harmonic oscillations of the output waveforms. The RNN models are contrasted with conventional digital Butterworth, Chebyshev I, Chebyshev II, and elliptic infinite impulse response (IIR) configurations. The RNN digital filtering schemes avoid introducing unwanted oscillations, which makes them more suitable for VF than their IIR counterparts. Finally, by utilizing the advanced features of scaling of the RNN model, we demonstrate that the RNN digital filter can be pH selective, as opposed to conventional IIR filters.

Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas

IntroductionUnderground coal fires pose significant environmental and health risks due to releasing CO2 emissions. Predicting surface CO2 flux accurately in underground coal fire areas is crucial for understanding the distribution of spontaneous combustion zones and developing effective mitigation strategies. In recent years, advanced machine learning techniques have shown promise in various carbon-related studies. This research uses an experimental approach to explore the power of advanced machine learning schemes for predicting CO2 flux in underground coal fire areas. ObjectivesBy leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies. MethodsThe study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics. ResultsThe NGRB model yields the best predictive performances with R2 of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.

Time-resolved photoelectron spectroscopy at surfaces

Light is still an important spectroscopic tool for investigating the electronic structure of surfaces. Time-resolved photoelectron spectroscopy has mainly been developed in the last 30 years. It is therefore not surprising that the topic was hardly mentioned in the last issue on “The first thirty years” of surface science. In the second thirty years, however, we have seen tremendous progress in the development of time-resolved photoelectron spectroscopy on surfaces. Femtosecond light pulses and advanced photoelectron detection schemes are increasingly being used to study the electronic structure and dynamics of occupied and unoccupied electronic states and dynamic processes such as the energy and momentum relaxation of electrons, charge transfer at interfaces and collective processes such as plasmonic excitation and optical field screening. Using spin- and time-resolved photoelectron spectroscopy, we were able to study ultrafast spin dynamics, electron-magnon scattering and spin structures in magnetic and topological materials. Light also provides photon energy as well as electric and magnetic fields that can influence molecular surface processes to steer surface photochemistry and hot-electron-driven catalysis. In addition, we can consider light as a chemical reagent that can alter the properties of matter by creating non-equilibrium states and ultrafast phase transitions in correlated materials through the coupling of electrons, phonons and spins. Electric fields have also been used to temporarily change the electronic structure. This opened up new methods and areas such as high harmonic generation, light wave electronics and attosecond physics. This overview certainly cannot cover all these interesting topics. But also as a testimony to the cohesion and constructive exchange in our ultrafast community, a number of colleagues have come together to share their expertise and views on the very vital field of dynamics at surfaces. Following the introduction, the interested reader will find a list of contributions and a brief summary in Section 1.3.

A case study of canal seepage quantification using gain/loss method and electrical resistivity tomography in an intensively managed water resource system in the Treasure Valley, Idaho, United States

Monitoring groundwater-surface water (GW-SW) interactions is essential for effectively managing the available water resources in semi-arid and arid environments. The focus of this study was to quantify how much SW is being exchanged with the shallow GW aquifer in the Treasure Valley (TV), Idaho, USA. Previous water budgets estimated regional canal seepage without incorporating canal variability and flow measurement uncertainty. To address this, we applied both direct (gain/loss) and indirect (electrical resistivity tomography (ERT)) techniques. Direct seepage measurements were taken on canals capturing a range of different characteristics in the TV. The discrete measurements were then used to estimate the total seepage anticipated to be lost from these canals during the irrigation season. Our findings showed high seepage variability across the canals. The ERT inversion approach was utilized before and after the irrigation season by applying an advanced inversion scheme to better constrain the canal seepage spatial variability and uncertainty by quantifying changes in the saturated zone with 2D-ERT results. Temporal changes in the subsurface resistivity were observed due to the lateral flow from the nearby surface canal during the irrigation season. The combination of these approaches improves our understanding of SW-GW interactions in intensively managed irrigation systems.

Triple junction benchmark for multiphase-field models combining capillary and bulk driving forces

Abstract A benchmark problem is formulated which is well suited for the validation of mesoscopic phase-field models for grain-boundary migration in polycrystals. First, an analytical steady-state solution of the sharp moving boundary problem is derived for a symmetric lamellar structure, which is valid for arbitrary bulk driving forces and triple junction angles. Characteristic quantities are identified to reduce the parameter space which in turn allows a systematic comparison of simulations and analytical results. Various multiphase-field (MPF) formulations are compared which approximate the sharp interface problem in terms of a diffuse regularization. An interfacial thickness convergence study reveals that the model error is largely dependent on the ratio of bulk to interfacial stabilizing force as well as the underlying model formulation. An additional grid convergence study highlights the efficiency of a more advanced discretization scheme. The results can be used to guide the selection of appropriate models and to estimate the interface thickness and spatial resolution required to achieve a given accuracy target. The post-processing framework consists of a fully automated determination of well-defined metrics from the phase field simulation data, eliminating human bias and facilitating reproducibility. The corresponding code is made openly available to assist the materials science and engineering community in validating MPF, multi-order parameter and similar model developments. We believe that this work provides a reliable benchmark procedure to better understand the potentials and limitations of current multiphase-field models as well as alternative approaches.

Empowering Fuel Cell Electric Vehicles Towards Sustainable Transportation: An Analytical Assessment, Emerging Energy Management, Key Issues, and Future Research Opportunities

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

Novel conformal structure-preserving schemes for the linearly damped nonlinear Schrödinger equation

In this work, by utilizing the Strang splitting technique, some advanced conformal structure-preserving schemes are developed for solving the damped nonlinear Schrödinger equation (DNLSE). The proposed innovative numerical approaches, namely the high-order compact conformal multi-symplectic method and the conformal momentum-preserving method, have two key computational advantages. Firstly, these two approaches excel in conserving local structures, and more importantly, they are capable of maintaining exact energy dissipation rates in any time-space region, particularly under periodic boundary conditions. To validate the theoretical properties and demonstrate the efficacy and stability of these approaches in long-time integrations, we conduct through extensive numerical simulations involving both bright and dark solitons.

Enhancing COVID-19 disease severity classification through advanced transfer learning techniques and optimal weight initialization schemes

ObjectiveMedical imaging plays a central role in medicine today by supporting diagnosis and treatment. For small medical image datasets, training from scratch is a time-consuming process and transfer learning emerges as a solution. In these cases, ImageNet weights are usually used as initial weights, after which fine-tuning is performed. We propose a methodology for COVID-19 severity classification (mild, moderate, severe, critical) in chest X-ray images based on transfer learning using DenseNet121 architecture as a base model. MethodsThe novelty of the approach mainly lies in the investigation of three different weight initialization schemes (i) ImageNet (ii) CheXNeXt (iii) DeepCOVID-XR, which are similar in a graded manner in terms of image nature, with ImageNet being the least similar, CheXNeXt similar as they were trained on different lung diseases not including COVID-19 and DeepCOVID-XR the most similar as they were obtained by training specifically on COVID-19 X-ray images. ResultsThe results show that the worst results are achieved using ImageNet as initial weights (average AUC = 0.700), followed by the better results with DeepCOVID-XR (average AUC = 0.774) while CheXNeXt performed significantly better (average AUC = 0.917). The results of weaker performing classification models were improved when the severe and critical classes were merged, to account for the similarity between these classes in the dataset (average AUC for initialization schemes were ImageNet AUC = 0.867, CheXNeXt AUC = 0.900 and DeepCOVID-XR AUC = 0.794). ConclusionThis leads to a conclusion that, in the medical domain, where image datasets are usually small and highly imbalanced, if initial weights are chosen to be in nature similar to the new dataset, achieved results are better. However, there is no need to start from weights obtained during training on the same disease, as this may cause overfitting. There are significant discrepancies between ImageNet and medical imaging datasets and the results from this paper could help in guiding future implementations of transfer learning in medical applications.

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on FengYun-3D MERSI-II daytime mid-infrared data

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on FengYun-3D MERSI-II daytime mid-infrared data

CuFe Cooperativity at the Membrane-Electrode Interface Elicits a Tandem 2e-+2e- Mechanism for Exclusive O2-To-H2O Electroreduction.

High O2 reduction reaction (ORR) kinetics and exclusive 4e- pathway selectivity are keys to realizing a sustainable society. However, nonprecious electrocatalysts at present cannot enhance the ORR turnover frequency and H2O Faradaic efficiency (FE) concurrently. To address these two challenges, hybrid bilayer membrane (HBM) electrodes with earth-abundant metal centers are developed to control proton-coupled electron transfer (PCET) in ORR. Here, an oxidase-inspired CuFe active site is supported on a tris(2-pyridylmethyl)amine HBM and explored as a unique interface for efficient ORR. This bimetallic HBM displayed an ORR activity 1.4 times higher than the monometallic systems and exhibited the highest FE for H2O (∼94%) among Cu-, Fe-, Ni-, and Co-based HBMs. Contrary to previous studies where the ORR current decreases upon embedding the metal center in a hydrophobic lipid environment, here, the incorporation of a nitrile-terminated proton carrier at the HBM interface boosts the ORR current by 1.7 folds relative to the case where the catalytic site is directly exposed to protons in solution. This intriguing dual improvement is supported by density function theory calculations where an additional 2e-+2e- mechanism occurs in parallel to the direct 4e- pathway, highlighting the synergistic effect of the CuFe HBM for facilitating high-performance ORR. A Zn-air battery is constructed using this CuFe HBM for the first time, further demonstrating that the knowledge gained from this HBM technology holds practical values in real-life applications. These findings on interfacial PCET are envisioned to spark new design principles for future catalysts with optimal electrochemical properties for advanced energy conversion schemes.

Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.

Spatial adiabatic passage of ultracold atoms in optical tweezers.

Coherent manipulation of matter waves, a distinctive hallmark of quantum mechanics, is fundamental to modern quantum technologies. Spatial adiabatic passage (SAP) is a prime example of this phenomenon, where a wave packet is transferred between two uncoupled localized modes by adjusting the tunneling coupling to an intermediate third mode in a counterintuitive sequence. Although this concept was introduced over two decades ago, its observation was previously limited to electromagnetic waves. In this study, we demonstrate this quantum interference effect using massive particles that tunnel between three micro-optical traps ("optical tweezers"). We begin by preparing ultracold fermionic atoms in low vibrational eigenstates of one trap, followed by manipulating the distance between the traps to execute the SAP protocol. We observe a smooth and high-efficiency transfer of atoms between the two outer traps, with a very low population remaining in the central trap. These findings open possibilities for advanced control schemes in optical tweezer array platforms.

Optimal robust configuration in cloud environment based on heuristic optimization algorithm

To analyze performance in cloud computing, some unpredictable perturbations that may lead to performance degradation are essential factors that should not be neglected. To prevent performance degradation in cloud computing systems, it is reasonable to measure the impact of the perturbations and propose a robust configuration strategy to maintain the performance of the system at an acceptable level. In this article, unlike previous research focusing on profit maximization and waiting time minimization, our study starts with the bottom line of expected performance degradation due to perturbation. The bottom line is quantified as the minimum acceptable profit and the maximum acceptable waiting time, and then the corresponding feasible region is defined. By comparing between the system’s actual working performance and the bottom line, the concept of robustness is invoked as a guiding basis for configuring server size and speed in feasible regions, so that the performance of the cloud computing system can be maintained at an acceptable level when perturbed. Subsequently, to improve the robustness of the system as much as possible, discuss the robustness measurement method. A heuristic optimization algorithm is proposed and compared with other heuristic optimization algorithms to verify the performance of the algorithm. Experimental results show that the magnitude error of the solution of our algorithm compared with the most advanced benchmark scheme is on the order of 10−6, indicating the accuracy of our solution.

Modelling and Validating the Nonthermal Plasma Parameters for Producing Liquid Hydrocarbon from Solid Polyolefin Wastes

This study solved a set of equations to verify the dynamic optimal conditions of nonthermal plasma (NTP)-chemical conversion of solid polyolefin wastes into liquid petroleum hydrocarbons. Furthermore, a novel optimisation model was validated with non-linear experimental conditions to assess the quantitative relationship between the process variables responsible for the degradation rate of wastes. The central composite design (CCD) experimental design was developed based on the Response Surface Model (RSM) technique. These techniques significantly improved the model predictions because of the more-detailed electrochemical description. Experiments were conducted in an in-house-designed and -developed NTP system with advanced data acquisition schemes. Both experimental and the numerical findings exhibited a good agreement, and the results indicated that the electrical factors of NTP could significantly affect the conversion yield (Yconv%) of solid polyolefin-derived wastes to liquid hydrocarbons. Additionally, the model investigation indicated that factors such as power discharge (x1), voltage intensity (x2), and reaction retention time (RTT) (x3) significantly influenced the conversion yield. After optimisation, a maximum conversion percentage (Yconv%) of ≈93% was achieved. The findings indicated that this recommended framework could be effectively employed for scaling the plasma synergistic pyrolysis technique for generating the maximal Yconv% of plastic wastes to yield an oil. Thereafter, the analysis of variance (ANOVA) technique was applied to examine the accuracy of the developed structure in order to upgrade this laboratory-scale processes to an industrial-scale process with >95% effectiveness. The calorific value of the produced oil was seen to be from 43,570.5 J/g to 46,025.5 J/g due to changes of the arrangements of the process factors, which specified that the liquid hydrocarbons showed similar characteristics like commercial diesel in this respect.

CYBER SECURITY FOR POWER GRIDS: THREATS, MITIGATION, AND FUTURE OUTLOOK

Cybersecurity is often confused with information security, although the latter focuses on human involvement, while the former sees individuals as potential targets and considers this an additional dimension. Cybersecurity discussions highlight important ethical issues impacting society and have led to the development of various frameworks addressing challenges like workforce development and personal data protection. This paper reviews these models, their limitations, and past mitigation techniques while offering future research recommendations. It explores vulnerabilities in wireless communication systems, the evolving nature of cyberattacks, quantum cryptography, and advanced key management schemes. Furthermore, it emphasizes the growing cybersecurity risks in power grids due to the integration of computing and communication capabilities into cyber-physical systems (CPS). A notable example is the 2015 cyberattack on Ukraine's power grid, illustrating the urgent need for improved security. This paper presents a comprehensive review of cybersecurity standards, emerging threats, and challenges in power systems. Index Terms—Cybersecurity, Cyber Threats frameworks, workforces, threats, techniques web 3.0, Implications

Oil spills characterization and modeling using remote sensing and geophysical techniques to protect the highly vulnerable coastal zones in Alexandria, Egypt

Nowadays, oil spills threaten both aquatic and terrestrial environments, especially in regions with intensive oil refining and shipping activities and high environmental sensitivity, such as Alexandria city, Egypt. Oil spill characterization in coastal populous cities is particularly difficult due to large chemical/physical soil heterogeneities and saltwater intrusion, which represent a major challenges for soil remediation and restoration. Recently, the development of inversion algorithms enables electrical resistivity imaging (ERI) to perform detailed characterization of near-surface soil pollution. The study implements an interdisciplinary approach using remote sensing and an advanced time-lapse 2D-inversion scheme for detailed characterization of oil spill patterns around oil refinery sites in the Alexandria coastal zone. The implemented scheme was able to improve the depth of investigation while maintaining the shallow lateral model resolution. The findings indicate that the mapped oil spills constitute a wedge-like form where the oil moves gradually downward, and it then shifts horizontally towards the shoreline with thinning in oil-contaminated zones under control of tidal action and ground surface slope. Consequently, guided by remote sensing observations, in-situ trenches/wells are suggested to withdraw the oil-contaminated water at the maximum deduced oil-contaminated soil thickness. The applied procedures in this study are replicable and can be effectively used as a pre-requisite to remedy oil spills along terrestrial coastal environments worldwide.

Bayesian optimization of proton generation in terawatt laser–CH2 cluster interactions within a plasma channel

Improving the energy efficiency in generating high-energy proton or boron ions is crucial for advancing the feasibility of neutronless laser-based proton–boron (p-B11) fusion reactions. The primary objective of this work is to optimize the fusion energy efficiency of a proposed advanced p-B11 fusion scheme. In the proposed scheme, an ultrashort laser pulse is guided by a plasma channel filled with carbon–hydrogen (CH2) clusters. The MeV protons are generated by the Coulomb explosion (CE) of the cluster, which, therefore, interact with surrounding boron to produce alpha particles. To evaluate the fusion energy efficiency under various conditions, 2D particle-in-cell (PIC) simulations are used, supplemented with analytical calculations and estimations. The Bayesian optimization (BO) algorithm is utilized to optimize the key interaction parameters. The BO approach allows us to identify optimal cluster and laser parameters that would have higher fusion energy efficiency.

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.